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See our breakdown of Kibo’s Monetate vs Certona to know the difference between each personalization platform.
Monetate and Certona are respectively rated the #2 and #1 Personalization Providers according to the Internet Retailer Top 1000. In the process of writing this article Kibo Commerce (which owns Certona) actually acquired Monetate. Kibo will be keeping the two platforms separate for the foreseeable future, so the question of Monetate vs. Certona is still very relevant for eCommerce businesses.
What do these eCommerce personalization engines do? Before we get into the specifics of each leading personalization engine, let’s take a moment to review what the general capabilities of tools like Monetate and Certona are.
What is a Personalization Engine Good for?
Certona highlights 6 key capabilities in their guide on Understanding Personalization Capabilities, these include:
- Collaborative filtering: Also referred to as the “Wisdom of the Crowd”, collaborative filtering does not focus on the individual behavior of shoppers, but rather collects data on how groups of people react to specific assets on your site.
- Customer segmentation: Segmentation allows you to target subsets of groups based on the specific attributes of those shoppers (such as customer intent and purchase history).
- Rule-based personalization: Business rules allow you to define, test and execute different experiments while still meeting your specific business goals.
- Real-time profiling: Profiling is one of the most powerful aspects of personalization, this helps you create individual shopping profiles in real-time and customizes the experience for those customers accordingly.
- Predictive modeling: Predictive modeling uses many techniques from data mining, machine learning, and artificial intelligence (AI) to predict the future behavior of shoppers.
- Data integration: Data integration is the process of leveraging data from various sources to gain additional insights into each and every shopper.
In the experience of eCommerce businesses we’ve spoken to, there are three main use cases that both Monetate and Certona can handle well, these include:
- Product recommendations
- Content recommendations (and content personalization)
- Dynamic UI changes (i.e. changing the navigation to match a customer profile)
Now that you know the high-level functionality and use cases for personalization engines, the question then becomes:
Are Monetate and Certona functionally different in terms of their capabilities, or are they extremely comparable platforms with different branding?
The short answer is: There are major differences in their capabilities as well as the users they are best for. To illustrate these differences, let’s discuss the use cases and unique features of each platform.
Note: Do you want to know more about getting started with personalization on your eCommerce store, or want to improve your personalization such as product recommendations?
Monetate vs. Certona: Use Cases of Each Personalization Engine
Although these two software providers are similar, they do have several unique features that they each specialize in. If we had to break down the differences of each platform in a single sentence:
Monetate specializes in testing and optimization, while Certona specializes in AI-powered personalization throughout the customer journey.
Is one tool better-suited for your eCommerce store’s needs than the other? Do you even need either of them? Let’s zoom in to review the unique features and use cases of Monetate and Certona in a bit more detail.
Monetate: Use Cases & Unique Features
As mentioned, Monetate is best known for its testing and optimization capabilities. The platform lets you launch controlled experiments to test everything from creative, recommendations, messaging, and UI changes. There are a few different types of tests you can run with Monetate, including:
- A/B testing: these are used to determine the better of two content, product, or UI variations.
- Multivariate testing: these are used to test more than two components of a website.
- Dynamic tests: these allow you to test multiple features, monitor results in real time, and then automatically allocate more traffic to the winners.
One of the unique aspects of the Monetate platform is that you can combine your own customer data with third-party data from their personalization exchange. The personalization exchange is meant to help solve the “cold start” problem faced by consumer retailers who don’t have existing customer data for the software to model off of.
By supplementing your customer profiles with third-party data from such as behavioral, attribute, and location, Monetate’s user data from external sales channels can help fill the gaps in your own data to enable the full use of the platform.
In addition to the data exchange, the platform also has an open architecture that allows for data integrations and APIs to feed data into the decision engine from your existing analytics systems.
Another one of the main use cases of Monetate is for personalized product recommendations (Certona also has this capability). You can either choose to manually curate product recommendations, or you can use their algorithmically driven recommendations if you have a larger product catalog.
Monetate makes product recommendations by factoring contextual attributes about the shopper. For example, in the screenshot below you can see several examples of customer attributes like the Lifetime Purchases and Most Frequently Bought Category:
In addition to customer attributes, recommendations can be made based on things like the weather, ratings, inventory status, audience segmentation, or almost any other attribute you deem appropriate.
If you want to read an example of a company using Monetate for product recommendations, check out this case study of Helly Hansen using their geotargeting capabilities to deliver customized experiences based on the local weather conditions.
Certona: Use Cases & Unique Features
Similarly to Monetate, Certona allows you to personalize product discovery by rearranging your catalog to feature the most relevant and complementary products at the top of the page.
One of the unique features of Certona in terms of product discovery is that you can engage customers with interactive content to help expose relevant products and build customer profiles (what Certona calls “exceptional content experiences”).
For example, in the screenshot below you can see a popup designed to help with audience building. It prompts the user to answer a questionnaire to help them find the right product:
Another unique feature of Certona is its predictive search engine, which populates recommended products based on a customer’s search term and buyer intent. As you can see in the screenshot below, the search bar becomes a visual recommendation engine:
In addition to personalizing the customer experience of your website, Certona offers personalization capabilities that can be applied to the following use cases:
- Brick-and-mortar shopping: allows you to connect online and offline customer data.
- Contact centers: lets you automate manual merchandising processes to help your representatives recommend relevant upsells and cross-sells.
- Email: allows you to send dynamic email campaigns that are tailored to each customer.
If you want to read an example of a company using Certona, check out this case study of PUMA, which used the platform for real-time behavioral profiling and personalized product recommendations for their large catalog without increasing labor costs.
Monetate vs. Certona: Pros & Cons of Each Personalization Engine
Since Monetate and Certona are the top two leaders in the personalization software market, they’ve also been extensively reviewed by consumers, so let’s look at a few of the commonly mentioned pros and cons of each platform.
Monetate: Pros and Cons
One of the main advantages of Monetate that many consumers highlight is its ease-of-use. Like any other software platform, there is a learning curve and more complicated tests do require a bit of web development, but many say that their WYSIWYG interface can easily be taught to entry-level marketers.
One of the limitations of Monetate that several consumers mention is their lack of training videos, and the fact that the platform is not built for running more complex tests that require extensive web development.
Pros of Monetate:
- More of a self-serve user interface: you can test virtually anything with relative ease including A/B tests, multivariate tests, and dynamic tests.
- Ease-of-use for building and deploying experiments in your website or marketing campaigns with an intuitive WYSIWYG interface.
Cons of Monetate:
- Several consumer reviews mention that the interface is relatively simple to use once you get the hang of it, but there is still a learning curve and a lack of training videos in the documentation to help you become a power user.
- The platform is not built for running more complex tests that require extensive web development.
- Pricing is not transparent.
Certona: Pros and Cons
Although users have reported in reviews that Certona is a bit harder of a platform to learn how to use, it excels in its scalability and integration capabilities compared to Monetate. As highlighted on the consumer review site TrustRadius, if your company offers a wide breadth of products and services and you want to personalize a large amount of the customer experience, Certona may be well-suited for you.
Rather than a hands-on platform that someone on your team works on, Certona is all about their account management. They will work with your team to optimize your personalization efforts. So if you’re looking for more of a hands-off, managed solution, Certona might be right for you.
One of the limitations of Certona is that while you can run A/B and multivariate tests, their Smart Test & Analytics platform does require you to go in and identify the winning test that you want to promote.
This is contrasted to Monetate’s dynamic testing capabilities, which identifies and takes action on winners without the need for human intervention (even if it may just be a few clicks). While they have complementary features, given the enterprise-level pricing, many businesses will likely not opt for a combination of Certona and Monetate.
Pros of Certona
- Well-suited for companies with a wide breadth of products and services that want the ability to run more complex tests.
- As highlighted on TrustRadius, their visual search has a strong return and improves the discovery process for customers.
- The strength of their personalization algorithms — they currently hold 13 patents for AI-driven algorithms.
- Many consumers say the account management is very helpful if you’re looking for more of a managed solution.
Cons of Certona
- As mentioned on Gartner, ease of deployment is a bit lower with Certona compared to Monetate (you will likely rely on Certona’s account management, while Monetate might allow you to have more of a hand in things).
- As with Monetate, pricing is not apparent on the site and may likely be tailored to your store and use cases.
Criteria for Choosing Between Monetate and Certona
Of course, we’re all going to have specific business needs for choosing a personalization platform, but we’ve broken down the process into 6 main criteria to think about when choosing between Monentate and Certona. These include:
- Platform Ease of Use
Monetate may be right for you if you want a simple interface that you can teach less-technical marketers how to use. Certona may be right for you if ease of use is not your top priority and instead want to be able to build more complex experiences with web development.
- Self-Serve Platform vs. Managed Solution
Since Monetate has a more user-friendly UI, it can more of a self-serve platform where you can be involved in setting up and testing different experiences. Certona, on the other hand, is a bit harder of a platform to learn how to use, but they are known for having excellent account management if you’re looking for more of a hands-off, managed solution.
- Use Case: Personalization vs. Testing & Optimization
While Monetate is still a personalization engine, it’s best known for its testing and optimization capabilities. Certona is less of a testing platform and more of an end-to-end personalization platform.
- Size of Your Product Catalog
Both Monetate and Certona can certainly handle large catalog sizes (for example, auto parts and accessories).
They are also helpful for handling updates to dynamic catalogs that merchandisers are often changing from season to season (for example: fashion), and the account managers on the Monetate or Certona team can take care of this big regular task that your internal team might not have the bandwidth for.
- Unique Platform Features
Both platforms have similar features like product recommendations, but there are several unique features of each platform that may be of interest to you. Namely, Certona’s predictive visual search engine and Monetate’s third party data exchange. If you already have and store data prepared well for personalization, you can plug in to Certona much easier — with less of a technical headache. If you are missing that data, Monetate can step in with their data exchange to fill in the gaps.
Price is, of course, another consideration for all businesses and some users report that Certona is priced slightly higher since it is more of a managed solution. However, neither company is transparent on price and they likely adjust pricing to individual clients depending on the business and use cases of the tools.
Summary: Monetate vs. Certona
There’s no question that one of the most powerful technologies in eCommerce right now is personalization in the customer journey. Personalization is more than just product recommendations; rather, it should be thought of as a steady delivery of unique experiences starting from the very first touchpoint to the end of the buyer’s journey.
Both Monetate and Certona can be complements as part of a larger commerce platform to enable personalization. That said, businesses will likely choose one platform over the other depending on the use case and how hands-on or hands-off they want to be rather than opting for both platforms.
That said, if you’re in a business that is constantly changing, (for example if you’re in an industry like fast fashion) you may feel like you need to hire someone full-time just to handle all the product recommendations. In that case, the price paid for AI assistants like the addition of Monetate or Certona might make sense to enhance revenue and customer lifetime value — especially compared to hiring a full-timer to handle that on your own team.
As Meyer Sheik, the former CEO of Certona said in a Q&A, one of the main challenges that retailers and brands face when it comes to personalization is that they lack the technology infrastructure and internal resources to stand something like this up on their own.
One of the key takeaways that we learned from speaking to eCommerce business owners about their experience with personalization engines is that whichever platform you go with, there is still going to be a good amount of technical training needed to get everything up and running. So make sure someone from your team gets a technical onboarding from the personalization platform’s team to be safe.
If you do decide to go with one of these personalization engines and want to get everything set up correctly, that’s where Inflow can help. We’ve helped leading eCommerce companies implement personalization into their businesses to help drive more purchases and revenue from their customers.
If you’re looking for a team to help you personalize your customer experience to help increase conversions, schedule a call with us here.
When consumers jump from online to the phone, it can be a frustrating experience. But it doesn’t have to be.
The post How to create a seamless cross-channel customer journey with call tracking appeared first on Marketing Land.
Have you ever started the purchase process online for a complex product like a mortgage or healthcare then had to call the company to get questions answered?
Usually, it goes like this: Fill out forms online then get stuck. Call, then repeat everything you put in the forms. Get transferred, repeat everything again. Find out you got transferred to the wrong rep, throw your phone across the room, pour a glass of wine and buy something nice for yourself on Etsy instead of doing grown-up things.
While it may seem this like this is done to intentionally torment you, the cause is usually an inability to pass data from online to offline realms. Here’s how you can create a seamless online-to-offline experience for your customers.
How call tracking platforms can help
Buying journeys are increasingly digital, but over-rotating to online self-service can be a major source of frustration for consumers who need help sorting out a complicated purchase. Many times, they are going to want to pick up the phone to talk to a person.
In fact, Invoca research conducted by the Harris Poll found that in considered purchase categories like healthcare and home improvement services, over a quarter of consumers prefer to complete transactions over the phone.
The danger comes when you play bury-the-phone-number to force people into a digital-only transaction — when a company only has automated communications and no option for human interaction, more than half of consumers (52%) feel frustrated and nearly one in five actually (18%) feel angry. That’s probably not the experience you are looking for.
When companies do encourage consumers shopping or researching online to call, they can run into different issues and new ways to frustrate them. When a customer goes from clicking your ad, hitting your website, to calling your business, that often creates a data gap with two primary effects:
- The call center has no context for the call, making it more difficult to provide exceptional service.
- Marketing loses track of the transaction and has no data to optimize the customer journey.
This is where you need a call tracking and conversation analytics platform to bridge the gap. It’s a critical piece of the martech stack for any company that makes sales, sets appointments, or gives quotes over the phone. Call tracking and conversation analytics platforms can not only analyze what’s happening on the phone to classify calls and identify conversions, but they also track the digital journey that leads up to a call so marketers can get both attribution data and customer journey insights that allow them to optimize cross-channel buying experiences.
Here are just a few ways you can use call tracking platforms to create a seamless cross-channel customer journey.
Route calls to the right place the first time
If a potential customer finds your company online and they are calling to make a purchase, you don’t want to route the call to a customer service rep. This not only wastes the customer’s time, but it also burns up valuable call center resources getting them to the right place. You can improve call conversion rates and ensure the best possible experience by getting your callers to the right destination quickly.
There are three common methods of routing calls with a call tracking platform that can help accomplish this. You may end up using one or all of these, depending on your level of routing sophistication and customer needs.
Routing with call treatments
Call treatments are one of the simplest methods of call routing and it can be accomplished with a call tracking platform or in your telephony system. You can route by asking a caller to respond to a question using key presses, usually something like, ‘for sales, press one. For customer support, press two’.
If your business has multiple locations, you can also route calls based on the location of the caller. This can be accomplished via the callers’ area code using your telephony tools, but this poses a risk of improper routing since people frequently keep out-of-area phone mobile numbers long after they have moved.
Using a call tracking platform, however, you can present each caller with a unique local number (based on their IP address, not their phone’s area code) on your website or search results to make sure they get to the right location. Some call tracking platforms can even use tag-based tools that will automatically identify and replace all of your phone numbers on a given web page so you don’t have to do it manually. While online users are all presented with unique phone numbers for tracking purposes, they are still routed to your desired existing phone numbers.
Route calls with combined data sources
The most advanced flavor of call routing uses a combination of digital data captured by a call tracking platform, third-party demographic data, and/or your own first-party data that lives in your CRM or other internal sources. Invoca’s call tracking platform accomplishes this through three features in the platform called custom data, enhanced caller profiles, and lookup tables.
Custom data is the umbrella name for any data captured by Invoca that fall outside of standard UTM parameters or required integration IDs. Custom data fields are customizable to your business and typically include information like customer IDs, product SKUs, and shopping cart cookies.
Enhanced caller profile data is third-party demographic data matched to the caller. Examples of this include age, home location, and homeowner status. Lookup tables enable you to upload first-party offline data using a match-value captured by an Invoca custom data field. By tapping into these rich sets of data, you can dynamically route callers to the best destination, eliminating call transfers and key presses often associated with calls to businesses.
Unify your online and offline data sources
To avoid data gaps that can cause a fragmented buying journey, you need to unify your online and offline data sources. Easier said than done, right? This isn’t always a simple task, but call tracking platforms that are integrated with other data sources and martech platforms can help you accomplish this.
Call tracking platforms enable marketers to tie consumers’ digital journey data to phone calls using online data collection and trackable phone numbers. By unifying this information in the platform, you can analyze digital and call data in one place. Many marketers who use call tracking also use integrations with their analytics platforms like Google Analytics and Adobe Experience Cloud to analyze, unify, and take action on data in one place.
Using the Invoca platform as an example, here’s how the data is captured and what it means for you. In the call report, you can see all of your inbound calls and call volume trends at a glance. Clicking on a specific call brings up the call details where you can see a unified view of all digital and offline data associated with that individual call. You’ll see information about the call itself like key presses in the IVR system, call duration, and the full recording of the call. This data is valuable to help segment your calls, such as sales versus support calls, and to understand your standard call metrics.
You’ll also get detailed information specific to each caller like their name, caller ID, and demographic information such as age and home address. You will also get customer journey data like ad exposure and webpage visitation. You can think of this as cookie or campaign data. For example, you can see exactly which paid search campaign and keyword led to a call. By tying the digital campaigns to the offline call action, you can now understand which campaigns are driving valuable phone calls.
Lastly, Invoca is able to analyze conversations and identify call outcomes in real time. Outcomes could include actions such as submitting an application or purchasing a product.
By using a call tracking platform to route your calls and unify online and offline into rich call profiles, you can get actionable insights to help you make more informed marketing decisions that can help create a friction-free multi-channel buying experience.
Learn more ways to create a seamless cross-channel customer journey in the Call Tracking Study Guide for Marketers.
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The new solution, available via Experian’s MarketingConnect, connects digital and offline identities to help marketers better understand their customers.
The post Experian’s new identity resolution service seeks to help marketers improve customer experience appeared first on Marketing Land.
Experian has announced a new solution aimed to help marketers connect online and offline attributes and better understand their target audiences. The solution leverages machine-learning algorithms and probabilistic techniques to connect billions of identity signals and data elements, including Mobile Ad IDs (MAIDs) from a variety of internal and external sources.
Why we should care
Identity plays a crucial role in helping marketers understand who our customers are. The ever-changing technology landscape, however, creates challenges for marketers trying to analyze their customers’ activities. Experian’s solution will allow marketers to bridge gaps in identity resolution and bring together the appropriate data points to reach customers with relevant, timely campaigns.
“The combination of hundreds of digital and offline touchpoints, disjointed technology and data silos make it difficult for brands and agencies to gain a single customer view,” said Kevin Dean, Experian’s president and general manager of marketing services, North America. “Consumers need to be at the heart of every advertising campaign—and proper identity resolution is critical to accomplishing that objective. The ability to connect these data elements, with consideration to data privacy, opens the door for brands and agencies to create and deliver personalized messages that are timely and relevant to their audiences.”
More on the news
- The new solution will be available via MarketingConnect, Experian’s identity resolution platform.
- The MAID resolution capability was developed in collaboration with Experian Data Labs, Experian’s advanced analytics and development group.
The post Experian’s new identity resolution service seeks to help marketers improve customer experience appeared first on Marketing Land.
The carefully evasive proposal included intriguing tidbits: Jeff Bezos laughed when Mr. Kamen assembled an It for him [. . .] The proposal also included proclamations from tech-world celebrities like Steve Jobs, Apple’s founder, that the device might change urban life and could be as significant as the development of the personal computer. The New […]
The carefully evasive proposal included intriguing tidbits: Jeff Bezos laughed when Mr. Kamen assembled an It for him [. . .] The proposal also included proclamations from tech-world celebrities like Steve Jobs, Apple’s founder, that the device might change urban life and could be as significant as the development of the personal computer.The New York Times, January 2001
Dean Kamen’s code name for the project was “Ginger.” That was all most people knew. But few could wait to learn more. Deprived of source material, journalists wrote articles about articles. Finally, in December 2001, came the big reveal: Ginger was the Segway.
The rest of the story is familiar. The buzz turned out to be mostly that—buzz. A similar disappointment has plagued many mysterious campaigns of decades past (like those billowing sheets that teased new cars—a bold choice given 1990s designs).
Secrets deliberately withhold information—briefly or indefinitely—for company benefit at consumer expense. Misused, they are P. T. Barnum–style gimmicks.
Deployed well, they capture attention, stoke curiosity, and dig an economic moat—real or imagined—in consumer minds. “Torment your customers,” Stephen Brown advocates, tongue only partially in cheek. “They’ll love it.”
Why secrets are powerful
Brown’s argument challenges conventional wisdom about consumers:
They do not want us to prostrate ourselves in front of them and promise to love them, till death us do part. They’d much rather be teased, tantalized, and tormented by deliciously insatiable desire.
Brown punched up his argument, no doubt, for attention. But psychology supports his take, especially when it comes to secrets.
The “secrecy heuristic”
Does a “CLASSIFIED” stamp make information more persuasive? Mark Travers, Leaf Van Boven, and Charles Judd argue it does. In a series of studies, they identified the “secrecy heuristic”:
This ‘secrecy heuristic’ can increase the perceived value and decision weight of information that happens to be secret, independent of any genuine differences in informational quality.
The authors assessed three ways that secrecy might impact perception. In every instance, information that was believed to be secret carried more weight:
- Experiment 1. “People weighed secret information more heavily than public information when making recommendations about foreign political candidates.”
- Experiment 2. “People judged information presented in documents ostensibly produced by the Department of State and the National Security Council as being of relatively higher quality when those documents were secret rather than public.”
- Experiment 3. “People judged a National Security Council document as being of higher quality when presented as a secret document rather than a public document and evaluated others’ decisions more favorably when those decisions were based on secret information.”
Why do we assume that secret information has more value? Throughout our lives, we learn that withheld information is often vital information: insider trading is lucrative; knowing someone’s limit in a negotiation is advantageous; we deem the information we keep secret to be important.
That primes us, write the authors, to associate secrecy with value:
people who believe information is secret may interpret that information differently—with greater credulity, for example—than do people who believe information is public. Second, secrecy may serve as an independent cue to quality.
The effect is especially powerful, they continue, when the “target attribute” of an object is difficult to assess, like the challenging foreign policy questions they posed to participants.
Complex products and decisions—rife in the B2B world—encourage us to default to our heuristic judgment. It’s why a “clients only” Magic Quadrant report from Gartner—not the public G2 Crowd reviews—may carry more weight when picking a martech tool.
When we hear secrets, we also pay more attention. Julie D. Lane and Daniel M. Wegner investigated the “cognitive consequences of secrecy” and found that information that we perceive to be secret is more readily recalled.
Compared to freely available information, then, secrets are persuasive and sticky. Their exclusivity also feeds our desire to be “in the know.”
Scarcity and reactance theory
Withholding information makes it scarce. As Cialdini argues,
This often adaptive mental shortcut is one that naturally develops, since we learn early on in our lives that things existing in limited quantities are hard to get, and that things that are hard to get are typically better than those that are easy to get.
In some cases, like Monet paintings, scarcity is real. In others, like diamonds, it’s been manufactured. Secrecy can manufacture scarcity: timed or limited releases for physical products, exclusivity for services.
Limited access enhances our interest. The idea is called reactance theory:
Whenever free choice—for example, of goods or services—is limited or restricted, the need to retain freedoms makes humans desire them significantly more than previously, especially if marketers can convince people that those freedoms are important.
As Brown writes, it’s a strategy that “makes ’em work for it, by limiting availability, by delaying gratification, by heightening expectations, by fostering an enigmatic air of unattainability. It doesn’t serve demand; it creates it.”
And yet secrets, it turns out, do make friends.
The bond of a shared secret
Once the consumers are finally permitted to have access to their desires, the secrecy involved with that offering might increase consumers’ attachments to what they have just purchased, to the firm that sold it to them, and even to the other consumers buying it.
That bond divides people into three categories: Insiders, Aspirants, and Outsiders.
Insiders, Aspirants, and Outsiders
The moment a secret exists, it creates Insiders. Insiders have the power to keep or divulge a secret. Insiders may include only those within the company (e.g., Apple employees before a product launch) or a subset of consumers (e.g., the first to purchase a new iPhone).
As Adam J. Mills explains, “the value of the secret to the Insider consumer is in terms of both exclusivity and empowerment.” Compare that to the lowly existence of an Aspirant:
Aspirant consumers do not know exactly what Apple has in store, but they anticipate, hypothesize, and desire to be in the loop. The value of the secret to Aspirant consumers lies in its scarcity, and this reinforces the value of the exclusivity to the Insider.
Outsiders, on the other hand, aren’t even aware that there is a secret. And the power of a secret requires that consumers know it exists. For marketers, then, the goal is to distribute that secret, in full or part, for maximum impact:
- To create real value for Insiders.
- To amplify perceived value to woo Aspirants.
- To spread awareness to convert Outsiders into Aspirants.
Not every secret is a ticking-clock campaign. In fact, the secrets that have helped differentiate brands are far more enduring.
The enduring secrets that build brands
David Hannah, Michael Parent, and Leyland Pitt catalog secrets by marketing value. In their quadrant structure, the most valuable secrets have marketing and strategic value; the least valuable have neither.
Marketers can increase the value of a secret in two ways:
- Romancing the secret via marketing campaigns that increase perceived value.
- Educating the secret with business-wide efforts to increase real strategic value.
“The marketing of secrecy,” they contend, “is about deciding what to tell and how to tell it, as well as what not to tell and how not to tell it.” Here’s what companies have been doing—with varying success—across the secrecy grid.
1. Appealing secrets have strategic value and marketing value.
Each July the Bintliff crew heads a boat out to the ‘ole mud hole’ and scoops up hundreds of pounds of the ‘Magic Mud’, enough for one season. Then the precious product rest in barrels until the next spring when it’s packed and shipped to each of the major league teams, minor league teams, most independent leagues and many colleges in time for opening day.
Their process can be learned and copied. Their fairy-tale origin story—one compelling enough to earn a feature on CNN—can’t be.
Take “Merchandise 7X,” the most guarded component of Coca-Cola’s secret recipe. Or so it would be, if the recipe were actually secret. The radio show This American Life unearthed the recipe, which had been published in 1979—down to the dram—in The Atlanta Journal-Constitution.
They made sample batches for the show, and less-frequent soda drinkers couldn’t tell which was the “real” Coca-Cola. (Habitual Coke drinkers fared slightly better.) When they brought the recipe to Coca-Cola’s archivist, Phil Mooney, he hedged on whether it was the “real” recipe.
Whether it is or isn’t doesn’t matter. Nuances in the production process of Coca-Cola mean that minute differences persist—detectable only by the Big Gulp demographic. But the secret isn’t the recipe.
The real value is the existence of a secret recipe, not the strategic advantage it affords, which is why Coke will never confirm nor deny the authenticity of any recipe. They lose little by the exposure of the recipe; they lose everything if they admit it.
What if you don’t have a nineteenth-century formula? The order of operations may surprise you. Often, powerful secrets don’t create the brand; the brand, after gaining prominence, creates the secret. So it is with KFC.
Here are—almost certainly—the oh-so-secret 11 herbs and spices (something KFC, of course, denies):
The recipe comes from a family album of Colonel Sanders’ nephew. The “secret” recipe didn’t start that way:
In the 1940’s, Colonel Sanders developed the original recipe chicken to be sold at his gas station diner. At the time, the recipe was written above the door so anyone could have read it.
Once publicly visible, it’s now treated as the Holy Bucket of trade secrets:
- The original handwritten recipe is (supposedly) housed in a 770-pound safe encased in two feet of concrete and guarded by video cameras and motion detectors.
- In 2008, KFC used a Brinks armored truck and briefcase marked “Top Secret” to transport the recipe while upgrading the vault.
- Two different suppliers prepare the 11 herbs and spices so that neither knows the full recipe.
KFC, in particular, exemplifies how older brands can repackage their historical processes. Every company has proprietary information, but, with a bit of marketing alchemy, you can turn “proprietary” into “secret” to powerful effect.
That’s what Bush’s Baked Beans did with their recipe. “Why is it kept secret?” asks Nick Greene. “Try to think of another company’s baked beans ad, and therein lies your answer.”
The value of a secret isn’t static. McDonald’s outed their own recipe for “secret sauce,” even promoting an instructional YouTube video from one of their chefs. Why?
The once-secret sauce—a simple blend of mayonnaise, sweet relish, mustard, white wine vinegar, garlic powder, onion powder, and paprika—clashed with modern interest in food transparency. McDonald’s, by divulging the sauce’s simplicity, portrayed their meals as akin to home cooking.
Secrets can go wrong in other ways. Dannon paid $45 million to settle a lawsuit after running a campaign that touted how its recipe for Activia yogurt had been “clinically” proven to aid digestion. (It wasn’t.)
Appealing secrets don’t have to be brand-defining to have value. Take any SaaS product. Every limitation consumers encounter—on a freemium version, via gated access to content—is a chance to hint at how much value is on the other side.
Companies tease this well with design, allowing freemium users a glimpse behind the curtain to stoke curiosity.
Cook’s Illustrated could fully gate everything, but who would know what they’re missing? How else could you turn Outsiders into Aspirants and motivate Aspirants to become Insiders?
Even when there’s scant real value to hide, there’s opportunity.
2. Mythical secrets have marketing value but no strategic value.
“As practitioners of high finance,” writes Ron Chernow in The House of Morgan, the Morgans:
cultivate a discreet style. They avoid branches, seldom hang out signposts, and (until recently) wouldn’t advertise. Their strategy was to make clients feel accepted into a private club, as if a Morgan account were a membership card to the aristocracy.
Mythical secrets are powerful creators of exclusivity: We crave membership, even if the primary member benefit is status.
Now, they disguise an email opt-in as “membership,” a framing that pads their email list and generates an incredible flow of data—every on-site action comes from logged-in users.
For Gilt, the angle makes sense: Exclusivity aligns with high-end fashion.
A Touch of Modern has followed the same path, requiring an immediate “membership” opt-in before users can browse products. The veneer of exclusivity persists despite the company’s regular TV spots and 14 million users.
The concept works at lower ends, too: Memberships at Sam’s or Costco prime our brains to expect good deals, even when they often aren’t that good. If it weren’t a good deal, we reason, why would it be available only to members?
A similar approach can aid beta launches, transforming a “please sign up” into an exclusive “invite only” or “early access” affair. A copy tweak—affecting as little as the call to action—creates value.
Mythical secrets can also rescue commodities. Because commodities lack appealing secrets, a farcical backstory—masquerading as a differentiator—fills the void.
The back of Old Spice deodorant details a faux secret that builds brand affinity. We know it’s nonsense. But they’ve exhausted any marketing value from their actual process, proprietary or not. Might as well dream up one that amuses the target audience.
CD Baby famously took a similar tactic with their confirmation emails:
It’s whimsical. It playfully suggests differentiation via extraordinary customer service—while creating real differentiation by entertaining its recipients.
For these companies, secrecy isn’t the linchpin of the marketing strategy, but if it’s enough to capture attention, it may do its job. There’s an opposite class of secrets, too—those with strategic value but no marketing potential.
3. Plain secrets have strategic value but no marketing value.
Plain secrets are competitive advantages your consumers don’t care about (yet, or possibly ever).
Google’s results are (usually) better, but how much do you really care about their algorithm? Would it make a difference if they advertised the number of ranking factors they consider? Or the comparative reliability of their servers? Or the speed of their crawlers?
Compare that to Pandora, a company that touts its trademarked algorithm as a way to increase your enjoyment of music:
Frequently, plain secrets “cannot really be used as an incentive for higher prices—it is more likely to be a cost reduction mechanism—and cannot typically be used to entice customers.” Walmart is an obvious example—we care about low prices, not the logistical networks that achieve them.
Plenty of patents fall into this category, which may get marketed meekly as a “patented process,” but the details of that process do little to influence purchasing decisions.
4. Weak secrets have no real value.
Weak secrets may act as short-term differentiators, but the benefits don’t last. These are “innovations” like stripes or sparkles on toothpaste—easy to copy and of limited influence.
All four categories of secrets have a common trait: The secrets are fixed, either withheld entirely (Coca-Cola, KFC), divulged after a conversion (Cook’s Illustrated, Gilt), or promoted in broad daylight (Old Spice).
Other secrets are on a countdown, with marketers trying to make the most of each moment leading up to a big reveal.
Leaks and short-term secrets that guide marketing campaigns
The best part of an NFL (or Game of Thrones) season, you could argue, is the off-season: The dissection of trade rumors and cryptic trailers, the conspiracy theories and predictions that fuel endless debate.
After the games are played and the episodes released, reality constrains conversation. The breadth of a hypothetical discussion is greater than a review of past action. And therein lies the marketing appeal of temporal secrets.
Apple has thrived on intentional leaks (and the prevention of unintentional leaks). An unreleased iPhone 4 was once left “accidentally” at a bar. Bits of information about new products show up on sites like MacRumors in the weeks before a launch.
Apple has even been accused of leaking a $1,000 price point for the first iPad to gauge consumer and investor sentiment.
There’s an entire taxonomy of intentional leaks, which hinges on whether a leak is factual and if a company admits to being the source of the information:
I just want to help people be smarter marketers and make better decisions for their clients [. . .] so we’ll have some stuff coming out that’ll make it easy, but you know what, don’t use it!
Because what I’m giving you is six months ago shit that you didn’t know yet, so we give away shit like that, and it’s “Wow, look at all that shit Seer gives away,” but now I’m bringing in census data, so go take my old shit because now I’m working on this, and I’ll release that in six months.
Most companies run drip campaigns for content: Regular blog posts, video series, etc., that gradually make internal knowledge public. Reynolds concentrates his knowledge-sharing into a “big reveal,” while also positioning himself as an Insider—one with the power, and generosity, to care for Aspirants.
Distilled uses a similar tactic at their SearchLove conferences. All speakers go on stage to reveal one “secret”—something so valuable that it cannot be tweeted; it’s meant only for attendees. At no other time do people pay so much attention. Every attendee also becomes an Insider, empowered to keep or share that knowledge.
Your company processes are secret, even if the reason they’re unknown is because you haven’t yet had the time yet to write up your strategies. The wider world doesn’t know that your withholding is unintentional—remissness still primes the pump.
Of course, you don’t have to give away any information to exploit a secret.
Denial marketing and Harry Potter
“We coined the phrase ‘denial marketing,’” Minna Fry, a former marketing director at Bloomsbury told The New Yorker. “We were extremely tantalizing—releasing little nuggets. If you were really lucky, you’d get the title!” Her denial campaign even withheld the page count.
Bloomsbury also teased a potential shortage of copies (a lie that would only increase the satisfaction of buyers “lucky enough” to get one) and “accidentally” released a few copies at a West Virginia Walmart. The children who discovered them, of course, made international headlines.
Surprises can work the same way. Rather than a traditional round of pre-release promotion, Beyoncé released a full album on iTunes in 2013 with no advance promotion. The sudden revelation spiked emotions—and sales. The album was the fastest-selling in iTunes history and the most successful launch of Beyoncé’s career.
There’s a common thread for denial marketing campaigns: They exploit existing public interest. The promotion strategy for Harry Potter and Beyoncé wouldn’t have worked for the first book or album. In contrast, one secret has universal appeal: the future.
The appeal of predictions
The future is a secret. All are Aspirants; none are Insiders. That’s why we love predictions. Which would you read first?
- “The 5 Trends that Affected Marketers Most in 2019”;
- “The 5 Trends that Will Affect Marketers Most in 2020.”
I’d argue that you’d make more money by reading the former—these are proven tactics that have influenced day-to-day operations, and most of us lag behind in one area or another. But the latter almost certainly would generate more shares and commentary.
Predictions don’t require a product or company with a devoted following. Expertise helps—my predictions on Olympic rowing would rightly go ignored—but if you don’t have it, you need only a handful of interviews or survey responses to incorporate it.
Secrets limit access to information. Good marketers create value by regulating that access.
Every business wishes for a secret that’s a total differentiator—an economic moat to create an impregnable castle. But marketers have succeeded with far less.
That is, in part, because the difference between “proprietary information” and a brand-defining “secret” is more framing than substance. Many powerful secrets are revisionist histories—mythical origin stories that protect a market position won through less romantic means.
In simpler forms, secrets tease value without giving it away—blurring all but the introduction to an article or displaying 10 of 2,000 results from a SaaS tool.
Bring Tantalus to your fruit tree. Fill the pool of water at his feet. Then, for an email address or a credit card number, offer him a drink.
Here we outline our method to personalize eCommerce sites using just Google Analytics and simple cookies. No elaborate third party tools required.
Enterprise personalization software is typically complicated, expensive, and possibly overkill for a majority of eCommerce companies. In our experience, this leads many to avoid or delay implementing personalized experiences on their sites, which likely leaves revenue — and a better conversion rate — on the table.
This process gives eCommerce brands the flexibility to “do” personalization on their own terms, from the spectrum of light personalization (a few custom experiences in specific situations) to as much personalization as they want (many customizations for large portions of their audience), all without bloated, expensive personalization platforms with inscrutable artificial intelligence or machine learning algorithms.
We’ve just begun developing this process at Inflow — we are actively applying it to client sites as we write this — but we’ve used these core concepts for a long time. Namely, our process is based on how we implement A/B tests for clients that aren’t using a 3rd party A/B testing tool.
In this article, we detail each step of our eCommerce site personalization process, using various eCommerce personalization examples throughout.
Note: If you manage an eCommerce business and want to know how this process could help your website personalization efforts, you can learn more and talk to our CRO team here.
Overview of Our eCommerce Personalization Process
Any eCommerce personalization process really just needs to do 2 things:
- Bucket online shoppers into specific personas
- Show custom (personalized) experiences to each persona.
Ours is no different, but let’s use some examples to get a sense of how this works.
First, a clothing store may want to know if an anonymous user is a man or woman. The user could (a) indicate their persona through their site interactions which (b) helps the store display products that they would be interested in.
Or, in a more subtle example, a yoga store would benefit from knowing whether a user is a beginner or an advanced yoga instructor. The bucketing of users (step a) in this case, is not as obvious as the man or a woman example above, but if they were able to do so, they could offer a personalized shopping experience (step b) for each group; showing beginner friendly items to one group and bulk discounts on commonly used items for instructors to the other group.
Our specific process for doing this breaks down into a 4 steps that we explain in turn below:
- Brainstorm and Bucket Personas
- Determine Characteristics and Site Behaviors
- Analyze Data
- Personalize the Website
Steps 1 – 3 are dedicated to the first goal: Bucket users into personas, which is the foundation of this process, while Step 4 is to show users a personalized experience depending on which bucket they are in (this is the easier, final step).
Step 1: Brainstorm and Group Personas Together
The first step is to brainstorm who your users are and bucket them into personas. This step lays the foundation for the entire personalization process because it determines the types of potential customers we’re going to personalize for.
It’s important to note that we aren’t tracking anything here, we are just making a hypothesis about who our users are based on their actions. By the end of this step we want to have solid hypotheses of what user personas could benefit from a custom site experience.
To use a specific example, one client we have, Mountain House, sells freeze dried food. They’ve noticed over time that two different types of customers (personas) use their products: preppers and backpackers.
Preppers, or survivalists, are buying goods and food for potential emergency scenarios. Backpackers, of course, are buying goods and food for backpacking and camping trips.
They use similar products but have vastly different needs and goals. But we may hypothesize that if we knew whether a given user was a prepper or a backpacker, we could increase the site’s conversion rate by showing a custom experience, and more relevant products, to each. For example, buying in bulk could be good for preppers while backpackers will want to buy individual items, perhaps in more variety.
The details of your personas will depend on your particular store.
Once we finish creating our personas, the next step will be to think about the site behaviors that can indicate to us which persona an anonymous user belongs to. This will allow us to eventually cookie each user into a persona so we can later show them the proper personalized experience.
Step 2: Determine Characteristics and Site Behaviors That Indicate Which Persona a User May Belong To
Once we have the buckets from Step 1 we need to figure out what actions or behaviors can help us determine which persona new, anonymous, users belong to.
Some examples of these actions include:
- Viewing a category
- Purchasing a product
- Entering information into the website (i.e. searching for something)
- Clicking on the navigation menu
- Engaging with a module that helps narrow products
- Subscribing to an email list to receive relevant content
We’ll start by finding some obvious actions. Continuing on with our hypothetical clothing eCommerce store from above, viewing men’s jeans or bras for example could be a clear and obvious site behavior that indicates which persona (men or women) an anonymous user belongs to.
In some situations, finding a difference between personas is more difficult. Consider the yoga store example from earlier where we’re trying to differentiate between beginners and instructors. If there aren’t products or categories that clearly differentiate between the personas (both may buy yoga mats and clothing for example), one option is to use personalized content to help us differentiate.
If a new, anonymous user site visitor signs up to receive basic yoga instructions, for instance, we could safely assume they belong in the beginner bucket. Likewise, you might have, or be able to write, content targeted specifically at instructors or more experienced yogis that will help you identify and personalize those user experiences.
We have even been able to bucket an anonymous user by promising future personalized content in an email opt-in form. The act of offering this content can often convince a user to indicate their persona by allowing them to choose to receive relevant content.
Step 3: Track and Analyze Data to Confirm Actions That Will Bucket Users Into Personas
In Step 3, we’ll track and analyze the customer data we’ve been gathering and use it to figure out what actions are strongly indicative of an anonymous user’s persona. We’ll use these to actually personalize our site customer experience later on.
How to Setup Custom Dimensions to Track Site Actions
In Google Analytics (GA), we can track user actions using custom dimensions. A dimension in GA is a characteristic of a user, session, or even hit (page view or event). Typical dimensions in GA include Page, Source/Medium, Device, etc. and are sent to GA as part of any page view or event.
Custom dimensions are similarly not sent on their own, but must be sent as a parameter of another event or page view. For instance, if you wanted to log that someone viewed Men’s products in a custom dimension, you would have to either add the dimension to the page view of men’s product pages, or add an event to any clicks leading to men’s pages that would include the custom dimension.
For more information on how this works, check out the GA Help Article on Custom Dimensions here.
Once the dimension is in GA, you are then able to view reports based on whether someone has the dimension set or not, with separate line items for each value in the dimension.
For example, the image below shows a custom dimension with various “Product Color” values for a particular client. If we hypothesized that product color choice was indicative of a certain persona, we could use this data to help us bucket users into personas.
So for example, in our men/women’s apparel store, clicking on a women’s category might trigger us to set a custom dimension identifying this user as a woman in the customer journey.
In our yoga example, someone visiting an article on how to do beginner yoga poses might trigger us to set a dimension identifying this user as a novice, while someone visiting an article on mastering acroyoga moves might be pegged as an advanced user.
Compare Stats from Different Custom Dimensions to Finalize the Criteria for Bucketing Users Into Personas
So what actions should we track that will hopefully indicate whether an anonymous user belongs to a given persona?
This is a critical part of the process, and here’s how we typically do it.
First, we think of some “anchor actions” that almost definitely indicate the user is in a given persona. These are the ones you’re sure of. For example, in our hypothetical apparel online store, it’d be something like adding women’s jeans to their shopping cart indicates they are interested in women’s apparel, and adding men’s jeans indicates they’re interested in men’s apparel.
But (and this is key) just having a few anchor actions is not enough because it’s very likely that a large fraction of your site traffic will never take that action. If you only have a small number of dimensions that lead to a user being bucketed into a persona but, say, 50% of your traffic never takes those actions, then your personalization effort just won’t apply to a large percentage of your traffic, which means all your effort in personalization may not be as impactful as it could be.
So, second, you’ll want to liberally think of many other user actions that could also indicate a user should be bucketed into a given persona. Let’s call these “candidate actions”. These can be any of the action types we’ve been discussing so far: clicks on certain pages, adds items to cart, downloads a PDF, whatever you think could indicate a user belongs to a certain persona. Create custom dimensions for them in GA, as well.
Third, let some time pass, ideally a few weeks to collect data in GA.
Fourth, now compare how well the data from your candidate actions line up with your anchor actions. Basically you’ll assume the anchor actions are the source of truth for whether a user is in a specific persona. So each candidate action can be compared to the anchor actions to see, whether there’s overlap between users.
For example, let’s say you think downloads of certain content indicates a user is interested in women’s products. You’d compare and see, of the users who took this downloading action, what percentage also took the women’s anchor action vs. the men’s at various touchpoints with your site. If a decent fraction also took the men’s anchor action, your hypothesis that downloading this content indicated they were interested in women’s products is not likely to be true, so we’d discard this candidate action as not very telling of which persona a user belongs to.
From a high level, our goal here is to pick a handful (3-5) of the most indicative and consistent criteria for accurate personalization without going into too much detail. Too many details can lead to conflicting signals and make personalization too complicated. At some point if we keep adding behaviors, we’ll either have to use an algorithm or manually choose a prioritization to follow.
It’s important to note again that we still haven’t personalized anything on the site yet. Step 1, 2, and 3 are all about figuring out exactly who our personas are, how to bucket them, and what types of actions we can use to personalize our site and product recommendations.
Step 4: Personalize the Website
At this point we have clear buckets for our personas and we know what behaviors we can use to bucket a new user into each of them. Now we’re going to leverage the data we’ve gathered and finally set up the personalization for our site.
This is the easy part! If you’ve done the first 3 steps well, a good marketer can easily identify tests/personalized recommendations that are likely to move the needle.
To do this, we’ll start building a list of hypothesis of custom experiences that we could show to the different personas that we think could increase their chances of converting.
- Change homepage tiles to show products that appeal to one or the other
- Prioritize navigation to show the categories most likely to convert or have a high AOV to one persona or the other
- Change messaging and value proposition language in real-time on product pages around certain products (e.g. in our prepper vs. backpacker example, the same food could be positioned as “long lasting” (prepper) vs “light and delicious” (backpacker)
- Show messaging around abandoned cart items on the homepage, in pop-ups or somewhere else on the site
- Segment to a separate email newsletter to deliver more relevant email marketing campaigns to certain personas
- Show a specific upsell on checkout, or somewhere else onsite so the right visitors see better related products (to increase average order value)
Pro Tip: We recommend setting up these cookies as you do the research in Step 3 so that you already start building your cookied user list. That lets you start putting out personalized content much faster after you finalize your criteria.
What If a User Takes Conflicting Actions
We should emphasize that it’s important to keep your final custom dimension list small to minimize the risk that users take conflicting actions (one action indicates they’re in Persona A, another indicates they are in Persona B). But if that happens, you can simply choose certain actions as “trump cards” that outweigh others. Alternatively you could also just not personalize the site for users who take conflicting actions. As long as you keep the action set small, this should only apply to a small percentage of your total traffic.
We’re excited that this method of creating personalized eCommerce experiences involves no 3rd party software (besides GA, which so many online retailers use already) and offers lots of customizability.
If you see how this could apply to your eCommerce brand, you can reach out to our CRO team here or leave a comment below.
Many UX designers prototype, but very few prototype with dynamic interactions. The difference it’ll make on your design is night and day.
Many UX designers prototype, but very few prototype with dynamic interactions. The difference it’ll make on your design is night and day.
ProtoPie is the only prototyping tool that allows you to add delicious interactions to your interactive prototypes. Rather than showing your users and clients a prototype with plain, boring click-interactions, you can now delight them with a high-fidelity one full of rich and robust interactions for mobile, desktop, and any other screen.
Not only will your prototype look and feel like the real app you want to build, but it’ll move like it at every tap and click. The way visual elements animate on each page will make your app come alive. See for yourself and explore these mind-blowing examples.
Using ProtoPie is quick and easy. Design your prototype in your favorite design tool, such as Sketch, Figma, and Adobe XD. Then, import it and start adding interactions on macOS or Windows.
The interface is simple to use. Add triggers and responses to objects with a few clicks and adjustments. You’ll be able to utilize the sensors, cameras, and keyboards in mobile devices for the richest interactions. All interactions in ProtoPie are fully customizable, regardless of how detailed and advanced they are.
On top of that, ProtoPie has team collaboration features that allow for commenting and revision history. You and your team will be able to improve every micro-interaction through each iteration.
Prototyping with dynamic interactions is the future. There’s more for designers to express in UX design than mere graphics and clicks. They need to express the lively movement of each interaction. These interactions aren’t daunting or time-consuming to create when you use ProtoPie.
ProtoPie’s Black Friday Deal
Get 30% off for the first year for the individual plan, and 40% off for the first year for the team plan. This exclusive deal ends November 29th, so act fast!
ProtoPie Studio runs on macOS & Windows, ProtoPie Player runs on iOS & Android while ProtoPie Cloud is accessible via the browser. This makes ProtoPie the only high-fidelity prototyping tool that covers all major operating systems.
Understand how customer brains work – these are the most important cognitive biases for e-commerce marketers.
The post 12 Cognitive Biases E-commerce Marketers Need to Know appeared first on Neuromarketing.
Understand how customer brains work - these are the most important cognitive biases for e-commerce marketers.
The post 12 Cognitive Biases E-commerce Marketers Need to Know appeared first on Neuromarketing.
New technologies are radically changing the way businesses interact with customers. As a result, customer expectations are constantly changing. Businesses should innovate and evolve in order to meet those expectations and build relationships that custo…
New technologies are radically changing the way businesses interact with customers. As a result, customer expectations are constantly changing. Businesses should innovate and evolve in order to meet those expectations and build relationships that customers value. To do this, businesses should rethink their approach to customer experiences and engagement. 84% of consumers consider the experiences […]
The post How to Use Personalization and Automation for Small Business Growth appeared first on The Daily Egg.
The company is offering three different subscription levels, including a free “Starter” subscription.
The post Acast Open launches to give brands an on-ramp to podcasting appeared first on Marketing Land.
The podcasting platform Acast has launched Acast Open, making available its podcast production offerings to any brand or publisher wanting to start a podcast. Acast Open includes three subscription models — Starter, Influencer and Ace — that come with different levels of support and analytics.
Why we should care
Adobe Analytics reported that podcast app usage grew 60% over the past year. Not only does this translate to new advertising opportunities for brands — the increase in podcast popularity opens the door for any company or executive ready to take their content marketing to a new level via a branded podcast.
“Digital audio brings a new and untapped audience who are not reachable via traditional media,” says Sally Yu, director of research and insights for BBC Global News’ APAC division, during a recent event organized by BBC News and Campaign Asia, “It brings additive value to the traditional media reach.” Outside of music streaming, the top three forms of audio content consumed right now are music (67%), news (50%) and podcasts (37%), according to a study commissioned by BBC News that focused on the commercial opportunities of branded podcasts.
If your brand — or CEO — has value to add to a larger industry conversation, a branded podcast may be the piece of content marketing that sets you apart from your competition and helps you reach a whole new audience. Platforms like Acast and Spotify’s “create a podcast” app aim to make it easier for brands to join the ever-growing list of podcasters.
More on the news
- Acast reports that any podcasts produced on its platform that appear to be attracting “a significant enough listenership” may be invited to join its premium network of podcast shows.
- Acast’s current network includes a number of popular podcasts, such as “My Dad Wrote a Porno,” “Forever35,” and “Wahlgren & Wistam.”
- Acast Open is the result of Acast’s acquisition of Pippa, a technology platform that provides hosting, analytics, and monetization capabilities for podcasters. Acast purchased Pippa in April.
- The free Starter model includes a podcast RSS feed for distribution, basic analytics and a basic website for the podcast. The Influencer level is $14.99 a month and comes with advanced analytics and YouTube and Spotify support. Ace, the most expensive offering at $29.99 a month, is designed for companies in need of more advanced podcasting tools.
The post Acast Open launches to give brands an on-ramp to podcasting appeared first on Marketing Land.