Want to create better experiences and brand loyalty? Lean on your data

For 2019, marketers will need to increase efforts at data consolidation to allow truly effective multi-channel strategies.

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Marketing leaders are in the throes of 2019 planning and there’s no doubt improving consumer experiences is among the top priorities for the new year. Data shows consumer expectations (and frustrations!) are on the rise, and brands are scrambling to understand to deepen brand-to-consumer engagement. This article will explore three ways brands can make the most of data to improve consumer relationships.

Understand that consumers are evolving, and grow with them

Recent data from a global study which polled 7,000 consumers shows a third of consumers expect brands to anticipate their needs before they arise, and a whopping 70 percent of global consumers are annoyed when every company correspondence is about making a sale. Likewise, Accenture reports 75 percent of consumers are more likely to buy from a retailer that recognizes them by name, recommends options based on past purchases, or knows their purchase history.

Now more than ever, consumers expect brands to understand them and treat engagements as evidence that they are committed to knowing them better as individuals. Whether it be through recommended products, engagement channel preferences (like social media or email), more relevant content (back to school tips or downsizing advice for empty nesters) or selecting better promotions (free shipping versus bundle deals versus free sample products), brands will benefit from going the extra mile.

Glossier is an excellent brand example of evolutionary marketing. In an extensive feature on its founder, Emily Weiss, Entrepreneur Magazine wrote:

Her customers lived on social, and her products are visual by design, which meant that, with the right tools in place, sites like Instagram could become Glossier’s R&D lab and marketing platform…the company began using Instagram to build mini focus groups and quickly create products based on what they learn.  

Make data-driven marketing strategies a consistent reality

According to six months’ worth of database, customer and engagement research, Forrester Research found that “‘data-driven marketing strategies are the new standard.” According to Forrester, “B2C marketers are collaborating with CRM teams to optimize customer relationships. Customer database and engagement agencies have used this opportunity to carve out a spot for themselves on agency rosters by keeping databases secure and compliant with global regulations, as well as creating real-time insights for marketers to act on.” The industry is seeing more proof that data-led strategies are working, but that doesn’t come without its challenges.

Many marketers find critical data is spread widely across various platforms. Sixty-nine CMOs from the likes of Tacori, Fidelity Life, Office Depot, recently shared their challenges in a study conducted by The CMO Club, with 42% of them citing customer data management as the most difficult aspect of marketing technology. For 2019, marketers will need to increase efforts at data consolidation to allow truly effective multi-channel strategies.

One such success story is Opel. The 110-year-old German automobile manufacturer, which prides itself on product and service innovations, looked to marketing technology and data to strengthen its pipeline. Ultimately, by developing a data model tied to a specific goal, the company’s prospect database grew 30 percent in four months, while the number of identified profiles increased over 473 percent.

Look at data as an opportunity by working backward from a goal to determine which data sets are meaningful. Intentional, focused data collection, consolidation, and output is possible when marketers are working to uncover customer trends and opportunities. Here are a few tips:

  • Create an audit of data collection methods and management systems. This focus allows you to truly understand what systems people use and how they use them.
  • Apply industry standards for your business and create data consolidation plans accordingly. This should be a fundamental driver of systems designed to collect, manage and store data for both short term and long-term basis.
  • Know the privacy laws of your state and country. For those companies managing personal information of Californians, the California Consumer Privacy Act of 2018 (CCPA) requires that certain data be available on demand as of January 1, 2020.  Legislation like this should be incorporated into your data consolidation strategy.

Experience is everything

CMOs know how critical it is to improve relationship marketing, with 62 percent of marketers noting it is the most important (or one of the most important) functions of their team this coming year. Of course, communication and engagement is a big part of that, with two-thirds saying their top marketing automation goal for 2019 is to speak to their customers in a more relevant way. That sentiment is echoed by an Adobe report: 33 percent of retailers cite “targeting and personalization” among their top three tactical priorities for the year ahead, higher than for any other marketing tactic.

So how does a brand build both the communication channels and experience they seek?  Here are a few things to consider:

  • Ensure sales and customer service teams are properly aligned: Nothing irritates a person more than being solicited to buy a product they’ve already purchased (or hated and returned). There are many opportunities for customer feedback and service requests to inform which marketing strategies are working or not. Be sure your customer service department is a part of marketing and campaign conversations – they’ll have real-time insights that are worth hearing.
  • Lead with your values: Digital marketing can be a double-edged sword. There is enormous value in connecting via multiple channels with your customers and learning more about them through data, but brands that don’t honor consumer preferences ultimately fail because their values are being compromised in favor of potential sales.  And, let’s face it, if you don’t respect things like opt-out requests or address poor consumer experiences, customers will melt away like post-Christmas snowmen.

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Publishers using real-time data can help their bottom line, here’s how

It’s time publishers caught up to marketers on the personalization front. These three strategies can help.

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Advertisers are keenly aware of the power of personalization. Personalized digital ads deliver up to 3x the consumer engagement of non-personalized ads. Some 69 percent of marketers say personalization is their top priority.

But just as marketers can craft messaging for one-to-one communications, publishers can customize their content to win over new readers and keep them there. Doing so requires access to real-time data. A Forrester Research report found that 67 percent of publishers believed real-time data was important to their efforts, but only 27 percent of publishers said they received such data.

To illustrate how it works, look at how publishers acquire new readers. A reader might stumble on an interesting headline on Twitter and then realize that they like The Economist. After some consideration, they might even want to become a subscriber.

When the reader first visits the site, the publisher may have just one opportunity to win over a reader by making the experience personalized, relevant and engaging. That requires real-time data that’s segmented into actionable audiences for content and ad personalization on the first interaction. Tailored content ultimately leads to a better user experience.

Here are three ways that that such real-time data can do that and impact publishers’ bottom lines.

1. Increase engagement, time-on-site and retention

It’s difficult to convert sometime readers (a.k.a. snackers) into subscribers, but real-time data can help publishers create the best experiences possible to boost their odds. Imagine this scenario: Behavioral data shows a visitor to your site has been shopping for a new bicycle, so you show them an ad for a Bianchi Infinito CV Ultegra. Sounds great, right? The problem is, the consumer just bought that bike offline, so it’s a wasted impression. The advertiser’s not happy and the reader isn’t either because it’s an example of clumsy targeting. There’s a lesson here for publishers. Most are obsessive about watching their metrics in real-time to see which content is catching on with users. Real-time tools can recommend articles to a reader based on their profile. Publishers can experiment to see the right number of articles that convert readers to subscribers. But editorial is only one part of the equation. Ads that are relevant and in a preferred format can enhance the reader’s experience, too. Real-time data adds another layer of relevance.

2. Boost programmatic revenues

For publishers, the formula for increased revenues is simple: more readers equals more revenues. By capturing readers on the first visit, publishers can create new revenue opportunities. Understanding who those readers are is also important. For example, a luxury jewelry brand might want to target upscale consumers for a holiday push. A publisher might realize that 15 percewnt of its audience fits that description. Since advertisers pay higher CPMs to reach such audiences, publishers can significantly increase their rates. Publishers can also create personalized content to increase time spent and encourage such readers to return.

3. Expand audience size

Publishers can use real-time data to increase their audiences. Personalized content can increase engagement, which can, in turn, boost content sharing. But that’s not all; publishers can also use their first-party data to target content at lookalike audiences who have a higher likelihood to engage with it. Such publishers are using real-time data to target lookalike audiences at scale.  Real-time adds another layer to such targeting. If a potential reader has a strong interest in environmental news, for instance, then a publisher can leverage breaking news on that topic in a sponsored post aimed at that reader.

It’s time that publishers caught up to marketers on the personalization front. The evidence for using real-time data is overwhelming. And publishers who are not yet using it will be at a disadvantage.

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Ask an SMXpert: New approaches in customization can build better analytics reports

Data-driven digital marketing expert Simon Poulton outlines opportunities in various solutions to customize dimensions for more focused analytics reporting.

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Simon Poulton SMXpert graphic

Data-driven digital marketing expert and senior director of digital intelligence at Wpromote, Simon Poulton, was one of the SMX East speakers during the “Making Your Analytics Work Harder And Smarter” session. There were a lot of questions during this popular program so Simon took the time to answer a few more. Here are five questions submitted by session attendees and his responses.

Do you have experience linking Google My Business Insights data into Data Studio in combination with Google Analytics?

Poulton: Yes, there are various solutions available here. If you’re just getting started, you can simply export the data from GMB Insights and pass this into a Google Sheet that has been synced as a data source within Data Studio.

For those looking for a more automated solution here, you can make this connection using a pre-built connector (Supermetrics released one earlier this year) or develop for the API yourself to pass this data into a sheet or a database that can be connected to Google Data Studio.

As far as I’m aware, there is no simple method to displaying this data coming from Google Analytics, and it would be challenging to tie this data to other user interactions on site, making it simpler to go with a GMB > Database (or Sheets) > Data Studio approach.

Any tips for integrating data that isn’t built into Data Studio automatically? For example, a call tracking platform that is not built into Google Data Studio.

Poulton: There are a number of platforms that do not natively connect to Data Studio – although based on how we’ve seen the number of connectors grow, I’d imagine more companies are looking to add a connection here. Many of these platforms – especially in the call tracking space, do have a native integration with Google Analytics where they pass back Event data to Google Analytics that can be tied to Client IDs and be unified with the rest of the user’s journey onsite. This is incredibly powerful and allows for the ability to connect these user actions with other data like an attributable source that can easily be visualized in Data Studio.

This is as simple as passing the Events for calls into Google Analytics from the 3rd party platform and visualizing these within Data Studio like you would with any other Event data. In general, if you can find a way to automate passing data into Google Analytics or Google Sheets, then there is a simple way to have this automatically update in Data Studio.

Can you import first-party audiences (e.g., loyalty members) into GA and/or Data Studio?

Poulton: On the surface – no, you cannot import first-party PII into Google Analytics or Data Studio. However, there is a method that you can use to append this type of data to users identified on your site using the Client ID.

Out of the box, Client ID is not an accessible dimension for matching to imported data. However, you can create this as a Custom Dimension, and work with your developers to push in the Client ID that Google Analytics has already created for you. The next step is to push this value as a hidden field with your conversions and create a list of key pairs where you identify the user within your CRM, along with the client ID and the customer state (Loyalty Member or not) – once you have this, you can upload this data using the Data Import function in Google Analytics to append this value to users. It’s important to note however, this is not retroactive and can only go back as far as you’ve been tracking Client ID as a custom dimension.

Once this has been configured, you can refresh your GA Data Source in Google Data Studio (to ensure you’re bringing in this new custom dimension) and start to bring this data into Google Data Studio. You can use it as a report itself showing the difference between loyalty and non-loyalty customers or you can use it as a Segment to isolate data for specific reports.

This is very similar to the pCLV Cohort Import example that I provided during our session on Making Your Analytics Work Harder & Smarter.

Have you tried using the “compare to” toggle for scorecard vs. blending data?

Poulton: No – and I’m not entirely sure what this would achieve. As far as I’m aware the “compare to” toggle is for time-based comparisons only. When blending data, we are using a key to join two similar data sets that make sense to be viewed together. If you’re looking at the difference between the two sources, then it likely wouldn’t make sense to blend them within a scorecard format.

 


Search Engine Land’s SMX West, the go-to event for search marketers, returns to San Jose Jan. 30-31. The agenda, packing more than 50 world-class speakers, teaches you actionable search marketing tactics you can implement immediately to drive more awareness, traffic and conversions.

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Back to basics: Measuring your social media efforts with unique acquisition channels

Segregating paid, organic and social activity in analytics clarifies the activity that drives which type of conversion.

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Most organizations are spending a considerable amount of money and resources on their social media marketing efforts. These efforts generally take the form of three types of effort – organic, paid and promoted (also referred to as owned, paid and earned). No matter how you label them, you should segregate them into three unique marketing acquisition channels in your analytics reports to correctly evaluate how effective your efforts are.

To segregate traffic driven to your site from various social media properties, you’ll need to configure custom channels in your analytics tool. (For a detailed explanation on how to do this in GA, see the Marketing Land article The Google Analytics Social channel is broken. Here’s how to fix it.

Defining custom social channels

Before starting any configuration changes, first decide not only the names of these new channels, but what they represent for your clients (both internal and external).

I’d recommend setting up the following three channels.

Paid social: Consists of any paid ads you are running on any social media property to drive traffic to your website.

Promoted social: All activity performed by your social media team where no additional marketing fees are required. Typical activities that fall under this channel include typical posting to your social media channels.

Organic social: Any activity that the general public (people not on your payroll) drive traffic to your site from social media. This includes a person clicking on a “Share This” icon on your blog post or perhaps just including a link to your site in a spontaneous social media post.

Once you’ve defined your social media channels, you need to define the medium definitions (for GA the utm_medium parameter value) to be used your generate a custom URL to track (see Google URL Builder). The great news is you don’t need to do anything for organic social.

Here are some typical medium definitions (required by your analytics software) or make up your own. Note you can use more than one to refine your analysis at a later date.

Paid social: Paid-social, Social-PPC, Social-CPC, Social-display, Promoted-post

Promoted social: Psocial, Promoted, Post, Tweet

Remember, these changes to your analytics tool are permanent and will remain in place unless deleted. They will not impact historical data.

Repeating the benefits of custom channels

With this additional information, you’ll be more effective at evaluating which content is performing best and you’ll be able to compare its performance across the organic, paid and promoted social channels.

 

Another advantage is it will be much easier to compare conversion rates and goal achievement between different channels. In this example, a Data Studio report was created to showcase different goal conversions by all defined channels.

It is only by segregating paid from organic from promoted social activity that a complete picture of which activity drives which type of conversion. From the above chart, the Paid Search channel generated the most contact forms being submitted, while social (organic) and paid social drove zero forms being submitted. In this case, it’s important to look further into your analytics reports for assisted conversions (does a site visit generated from Paid Social come back to the site later through another channel) to determine the influence of Paid Social on contact forms being completed or other conversion points.

Any questions?

I hope you found this useful, and hope you’re as thrilled as I was to be able to segregate my client’s social traffic into meaningful refined channels. We are now able to put a true value on all our social marketing efforts (paid or promoted) and to calculate a realistic ROI which makes all business owners happy.

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Using data responsibly doesn’t have to weaken marketing strategies

In today’s diverse marketplace, businesses need data solutions that empower them to anticipate and respond to many circumstances and challenges.

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Data privacy is, as it should be, high on the minds of tech companies and the public at large. Unfortunately, much of the discussion of late has centered around controversies originating with the misuse or fraudulent nature of some data sources. But we shouldn’t let this cloud our collective judgment about how data can help businesses make better decisions.

When processes and strategies are in place to ensure the gathering and use of data is done responsibly, we can do right by consumers and brands. The benefits and opportunities made possible with accurate, well organized and actionable data will benefit the entire industry.

Brands must put data privacy at the forefront

Recently I was honored to speak at the Responsible AI/DI Summit where I addressed these issues and clarified the importance of brands ensuring that their consumer data strategies are a top priority. Brands have a responsibility to be forthright about the types of data they have access to, the permissible uses of that data and the value that data can provide. Developing data strategies that are useful but respectful to consumers must be at the forefront of every business today.

Brands are under tremendous pressure to make quick and important decisions based on data, but they shouldn’t lose sight of the trust that is placed in them and their partners to be transparent about what data is being used, how it will be used and how it will be gathered. And, with a supercomputer in every consumer’s pocket, data is available at a rate never seen before. When combined with the processing power to make sense of it, businesses can make smart, responsible use of the latest in AI technology to target and segment their audiences with strategies that prove to be a pragmatic approach to reaching their current and even new customers. Though consumers today remain weary of how brands are using their data, they are also more willing to engage with trusted brands who handle their information responsibly and provide more tailored and customized experiences. According to Accenture, 92 percent of U.S. consumers believe it’s extremely important for companies to safeguard their information, though 44 percent of U.S. consumers are frustrated when companies fail to deliver relevant and customized experiences.

Balancing trust with providing personalized experiences is something that any retailer handling consumer data today must understand as a business imperative.

Healthy approaches to data framework

To reach the full potential with the aid of data, businesses must also adhere to a healthy approach that accurately weighs the authenticity and quality of data available. Before data can be applied to business decisions, it must be refined, analyzed and measured against specific business challenges.

Once data is ingested and stored, it is critical for enterprises to remove low-quality data and refine the remaining data into usable, contextualized data sets that can be applied to analyze a number of key business challenges. Raw, unfiltered data will lead to confusion. But data that is scored, screened, filtered and then contextualized can be leveraged for high-level analysis that will reflect optimum levels of confidence for an organization and its partners.

Data should empower confident business decisions

Mobile location data is among the most rich and impactful datasets available today and when businesses analyze it effectively and responsibly they can compare different opportunities, such as prospective store locations, product localization strategies and holiday promotions.

In 2018, with Amazon knocking on every retailer’s front door with a commanding share of the ecommerce market, the strategies put in place to gain a competitive advantage should without a doubt include a thoughtful marketing strategy that encompasses responsible data practices.

Businesses of all types should embrace the use of data and be focused in their approach. Don’t let a few bad apples or outright nefarious examples of data manipulation cause you to miss out on this opportunity. Understanding the benefits and challenges of data is key, and the sooner enterprises understand this dynamic the better. In today’s diverse marketplace, businesses need data solutions that empower them to anticipate and respond to many circumstances and challenges.

The types and lasting effect of decisions that enterprises must make are expanding and if a leader doesn’t have all the available information to make those decisions it can hurt their business and customers. If you aren’t considering all the options and ways in which data can help you, I strongly encourage you to embrace the challenge and learn how the responsible use of data can reinforce your principles and help your business grow.

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Did the data-driven era miss an exit?

If the focus is on the right data aligned to business strategies instead of the same basic demographics, marketers don’t have to live in fear that inaccurate data will derail campaigns.

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people crossing in crosswalk

According to headlines, martech embodies technical sophistication, touting thousands of companies fueled by sumptuous features like artificial intelligence, virtual reality, personalization and more. Yet, a recent study found that what marketers really want is more, high-quality demographic data. I can’t help but think, did the data-driven era miss an exit?

The modern marketer can use thousands of pieces of consumer information — demographics, purchases, hobbies, social activity, geolocation etc.—but many marketing efforts are still based on the same basic demographics of age, gender and income. The status quo may be considered the easier, more risk-averse route. But if we stay in autopilot, marketers will slowly lose speed, falling behind for failure to embrace new technology in the spirit of innovation.

Time for an oil change of our way of thinking

First of all, I don’t propose we do away with demographic data, as it is incredibly powerful. But it only represents a sliver of the information available about a person. Furthermore, the accuracy of demographic data is already in question, and the cost of its poor quality can add up for marketers.

If a marketer over-relies on only a few demographic data points when defining an audience, accuracy of those specific data points is mission critical. But with a more comprehensive approach to defining an audience, where thousands of data points are considered, no single data point is at risk of toppling the operation.

No junkers, no hidden gems

There are hundreds of data aggregators in the martech ecosystem, specializing in CPG, behavioral, online, offline data and more, and many claim theirs is higher quality than others. These claims should make marketers wary. For example, I recently read an article claiming online purchase data is the ideal source of truth because it’s tied to a tangible transaction. But for a retailer that wants to drive new, incremental purchases, an advertisement targeted to someone who already bought is not ideal. In this instance, the quality of the behavioral data is a moot point because it doesn’t align with the retailer’s objectives. Further, when marketers have thousands of data points at their disposal, it seems risky to put so much weight into a single point.

Get into gear

Too often marketers question accuracy and quality before knowing what they want to achieve. Take, for example, a TV manufacturer that wants to find new customers. Using recent online purchases, even if the data is 100 percent accurate, won’t find many new customers. Would an audience of men that’s 100 percent accurate fair much better? Or an audience of people who live within a 5-mile radius of an electronics store?

Accuracy is important. But an audience with perfect accuracy that isn’t tied to campaign objectives won’t drive performance.

What if, instead, the manufacturer considered past purchases as one piece of the puzzle — a way to group its valuable customers. Then it could consider the thousands of additional data points available about those customers to find the commonalities that comprehensively represent the ideal customer. This audience mitigates the risk that a single data point’s accuracy will negate performance, considers all relevant data points, and bases the audience on the goal of driving purchases.

Who’s behind the wheel

With the right data aligned to strategies and in concert with business outcomes, marketers don’t have to live in fear that inaccurate data will derail campaigns.

When it comes to performance, it’s not just about data accuracy, but comprehension, recency, longevity, frequency, scalability and more. Once marketers are able to consider all of the data at their disposal, the martech industry can get back on track to the destination that the data-driven era promised, one where data drives smarter, one-to-one decisions that improve efficiency and results.

This story first appeared on MarTech Today. For more on marketing technology, click here.

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The decisioning dilemma: Getting the right combination of marketing tech to optimize engagement

Being able to bring together the facets of customer engagement – and to do so automatically – is the true value of decisioning tools.

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arrows on ground to choose directionWith the proliferation of martech and adtech tools over the past ten years, there’s no shortage of technology platforms available to help marketers understand, target and communicate with consumers more effectively. The sheer volume and range of tools can be downright dizzying: tools for planning and developing, tools for attracting and engaging customers, tools for identity and data management, as well as database tools and consumer-facing platforms. Combine all these with near-ubiquitous CRM and adtech platforms, ad networks and DSPs, and marketers have a suite of technology solutions that would be daunting to even the largest enterprise-level team.

But marketers don’t need to implement every tech tool available. They just need the right combination of tools to help automate intelligent decision-making.

Gathering information

As with any decision-making process, decisioning in marketing starts with gathering accurate information, specifically customer data. Creating a single customer view by gathering data across all touchpoints is crucial for facilitating personalization and contextual marketing, and various martech tools help marketers efficiently collect this data.

Some of these tools leverage AI and machine learning to comb through reams of seemingly-unrelated data and pulling out recurrences and patterns that generate deeper insights into customer behavior, and also point to new markets, segments, or other opportunities. Making sure that data is correct and being manipulated correctly at every point in the process – validating the inputs – is obviously of critical importance.

The availability of increasingly extensive data sets, driven by the prevalence of martech data collection tools like CRMs, give marketers the ability to gather the information needed to fuel their decisioning. Identifying which of these tools works best for a given organization is simply a matter of preference, one which a technology partner can help with.

Once the thousands, yes thousands, of potential data sources are integrated into a marketing framework or system, and that data is captured to create a single customer view, the next step for marketers is to apply automation layers to that data in order to leverage it for more effective marketing to customers and influencing their behavior.

This is the crux of the decisioning process.

Taking action

Decision management systems have existed among core business processes for some time, but they are now being marshaled to execute consumer engagement processes. By governing the parameters and timing of both outreach and interactions, these systems allow marketers to create a unified, consistent process. And automating that process with next-generation martech tools is the next phase of this evolution.

With automation, decisioning systems can deploy real-time offers based on predicted behaviors, manage and update databases without prompting, and execute intelligent multichannel campaigns without hands-on intervention. By removing that burden from marketers, they will be able to devote more resources to big-picture strategy, more time to think and problem solve, and refine and improve their high-level approaches.

A good decisioning tool will be able to track what type of content consumers are most interested in, allowing marketers to deliver more personalized, natural, tailored marketing content. It can be leveraged to manage a wide range of marketing activities, from customer data evaluation to offers and promotions to loyalty program interactions.

Being able to bring together these facets of customer engagement – and to do so automatically – is the true value of these decisioning tools. While a completely autonomous marketing system may still be some time away, today’s available decisioning tools can and do manage to take a significant portion of the day-to-day engagement responsibilities off of marketers’ plates. And linking these outreach-centric systems with existing data collection and management systems can create an even more comprehensive solution.

Getting together

All of the tools, data sources, analytics packages and marketing technology software services available to marketers presents a challenge regarding integration. Will AI-enabled systems become the one tool that can manage the rest of them – integrating insights, eliminating “siloed” data sets and facilitating personalization? Maybe. But while we figure that out, marketers can identify the right technology partners that can provide complementary systems to achieve these ends – starting now. A robust CRM, coupled with a decisioning system with automation capabilities can get marketers as close to the ”set it and forget it” ideal that AI and other advanced technologies promise.

With that combination of martech tools, marketers can optimize their consumer engagement, improve their long-term customer relationships, identify and reach new segments, and ultimately boost their revenue and profitability. With those potential impacts in the balance, using martech to facilitate automated decisioning is one of the best decisions a marketer can make.

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Unlock multi-touch attribution with CRM campaign tracking

If you’re using a CRM to house leads then you likely have the campaign tracking tools for middle funnel activity even if you have an offline transaction point.

The post Unlock multi-touch attribution with CRM campaign tracking appeared first on Marketing Land.

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Image credit: www.Curata.com

Brands with an offline transaction point often struggle to measure the full customer journey from acquisition source through to revenue. Often times, if revenue can be attributed back to something, it’s to the marketing channel responsible for the lead. This measurement is usually implemented with either a first or last touch attribution model for channel attribution and completely leaves out the rest of the customer journey.

The missing visibility and measurement is post lead acquisition. Once a lead enters our systems, how do we measure the effectiveness of our campaigns and lead treatments? Even better, how do we attribute revenue to content pieces and treatments applied to that lead in-funnel?

The answer may be something we already have access to. If you’re using a CRM to house leads then you likely have the tools to track middle of funnel activity at your fingertips with Campaign Tracking.

What is campaign tracking?

Campaign tracking within a CRM is technically an entity or object that tracks a variety of information about an event, mailing, emailing, or other marketing initiatives. It’s basically a container that houses all of the components of a campaign across channels and treatments.
Leads and contacts can be members of one or more CRM campaigns allowing visibility into the effectiveness of both single and multiple campaign influence across all levels of our funnel from lead to cash.

campaign graphic
Image credit: www.Salesforce.com

What are the benefits of CRM campaign tracking?

Depending on the CRM and how it has been architected and implemented, campaign tracking provides a significantly deeper level of insights and measurement including the ability to

  • Tie marketing activities to our sales pipeline
  • Compare the effectiveness of different marketing initiatives and their influence on each other
  • Measure mid-funnel activities alongside marketing channel attribution
  • Measure the effectiveness of content post lead acquisition
  • Inform the sales team of historical marketing activities via the contact record
  • Roll-up similar lead sources into a single object
  • Connect online & offline activities
  • Enable holistic ROI reporting
  • Preserve data integrity & maintain hygiene
  • Enables multi-touch attribution modeling within your funnel

Attribution and CRM campaign tracking

Now that measurement is enabled at such a granular level in-funnel we can see marketing activity influence across the funnel from lead to customer. This is where attribution really gets complex! Similar to the first-touch, last-touch, multi-touch debates on marketing channel attribution. Now we have these same debates in-funnel when attributing back to campaigns within our CRM.

Do we give credit to the campaign that initially acquired the lead?

Do we give credit to the campaign the lead responded to before they converted to an opportunity?

Do we give credit to the campaign that influenced the lead right before they converted to a customer?

First-touch attribution model

First-touch is pretty self-explanatory. In this attribution model, all credit is given to the very first action taken by the user that created the lead record.

attribute graphic
Images credit: www.Curata.com

The first-touch attribution has its advantages, it’s super easy to implement. The lead is tagged using a custom field and that field rides on the record all the way through the funnel to closed won. However, this model leaves so much of the story out neglecting to consider all other interactions the user had or actions the user took beyond that initial entry into the database.

Last-touch attribution model

Last-touch attribution is the opposite of first-touch. Instead of giving all credit to the first action the user took, we’re giving it to the last step the user took. Also easy to implement by merely over-riding that custom field.

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Last-touch is fantastic for measuring the effectiveness of campaigns targeting the bottom of the funnel geared directly toward driving a purchase decision. However, if we only look at what ultimately turned into a sale, we really lack insights into what levers to pull to get them to that point and we are leaving a ton of opportunity on the table. We are also boxing ourselves into diminishing returns and expensive tactics and are unable to scale our marketing programs.

Multi-touch attribution model

Multi-touch models are more complex, recording all interactions and giving credit to all touch points in the journey. They provide the clearest picture of attribution and provide the most insights regarding what levers to pull across the funnel to improve velocity and efficiency of our marketing investments. To implement a multi-touch attribution model within your funnel you have to utilize CRM campaign tracking. This is the biggest benefit of the campaign tracking tools within your CRM.

CRM campaign tracking reports

Once campaign tracking is properly set up and working within your CRM and when used in conjunction with a clean and granular lead source strategy, CRM campaign tracking opens up rich and robust reporting options. Your data will tell a very different story!

Here are a few sample reports that can be generated when both campaign tracking and a clean and granular lead source strategy are applied within Salesforce.

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I’d love to hear how you are utilizing campaign tracking within your CRM and what kind of new insights you’ve been able to pull. My guess is that once you were able to get to this level of insights, marketing resources were moved around to concentrate on what you didn’t even know was working within your marketing program.

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When it comes to campaign design and measurement, many sizes fit all

When you focus on tuning one campaign KPI, you inevitably affect the others. It’s an imperfect world where you must be aware of the tradeoffs you’re making.

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Crafting the right digital strategy to hit your ultimate marketing goal is a balancing act. You need to vet platforms, allocate budget and determine appropriate campaign KPIs.

This last point is seemingly the easiest element of a campaign to settle upon. However, focusing on the wrong KPI or not understanding how various KPIs interact with each other may negatively impact campaign success.

In today’s advertising marketplace, where the tech-stack can provide innumerable campaign measures, digital marketers need to put extra care into fine-tuning their campaign KPIs to help ensure that the scale they need is not limited by the measures they put in place.

The ideal versus the reality

Wouldn’t it be perfect if every campaign could be tuned so it delivers 100 percent viewability, zero percent invalid traffic (IVT), 100 percent in-demo targeting -– and deliver in-full while hitting click-through rate (CTR), cost per acquisition (CPA), or video completion rate (VCR) goals?

Marketers, like everyone else, must operate in an imperfect world. There are tradeoffs –- and these tradeoffs might mean altering or changing the weight placed upon various campaign KPIs to help ensure success.

A one-size-fits-all approach to campaign design and measurement does not always work, and it certainly does not always work for a single advertiser under every condition at all times of the year.

As we move into the months where marketers are executing their Q4 strategies, this is especially important to consider. Most brands this time of year need scale to affect the buying habits of as many consumers as possible. More than ever, a finely-tuned advertising strategy with strategic campaign KPIs is necessary to help ensure the ultimate opportunity is not hindered by restrictive or competing measures.

Performance measures, whether an advertiser’s campaign KPIs or a supply partner’s benchmarks, are the currency by which we evaluate the efficacy of the advertiser/media partner relationship. Their critical importance to the relationship reinforces the need for careful measurement planning and design.

Advertisers should carefully balance strategic campaign performance measures such as acquisition, brand impact, and video completion with tactical delivery measures of viewability, brand safety, and in-demo performance. The balance struck between measures will vary for each advertiser and will likely be impacted by overall marketing objectives.

How to strike a balance

Striking a balance does not mean abandoning one measure -– such as viewability -– for the sake of another. Marketers should recognize, however, that there is interplay between measures. And that the focus on one measure may impact another.

Advertisers have the right to demand -– and media partners have the responsibility to provide –- a high-quality and effective advertising environment. As we head into Q4, it is important to review overall marketing objectives and how they translate to individual campaign KPIs. Adjust where necessary and work to understand how a focus on a specific KPI has the potential to either enhance or detract from another.

A poorly-designed program with conflicting KPIs may potentially limit your reach and hand customers over to a savvy competitor. Consider how the KPIs you focus on could impact the return on your media investment.

In the end, you want to use your media spend as efficiently as possible to scale your programs to engage as many potential customers as possible.

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Bring order to chaos: Wrangling data for actionable insights

How to bring an overwhelming amount of data under control and use the insights gained throughout your business.

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Producing actionable insights is one of the most challenging issues that brands face today. Urgency is ever-present, pushing marketers and analysts to rush decisions. But urgency is only half of the problem. Making the situation more chaotic is the fact that we are simultaneously awash in waves of data from too many sources. Between the urgency to produce results combined with the massive sea of data, we are inundated us every time we wade in and then simply washed back to shore.

So where do we start? Transactional, engagement, or demographic data? Prospecting or retention? The inundation keeps pushing us back.

There are strategies to navigate the churn and turbidity, and remedy those issues. Sometimes we simply need to take a step back, narrow our focus, and even get a little ruthless.

Insights begin with goal-setting

First, we need leadership teams to get ruthless with what really matters. Analytics can’t chase the shiny object or rely on some utopian commerce breakthrough — if only we could find attribution in some rabbit-hole metric.

Think bigger. Get brutal with company and divisional goals.

Great goals have a couple of key characteristics in common. First, they’re specific — they have clear expectations and a path forward to measure and show success. Great goals also unify teams instead of dispersing them in different directions where everyone has a separate idea of how they can accomplish them.

To reach goals, every single person needs to be pulling the boat in the same direction. Great goals produce unity, which in turn helps to focus analytical firepower where it matters most. Remove the rest.

Where to start

The lowest hanging fruit is almost always customer retention. It’s the easiest behavior to shift; it has the most room to grow; it’s the most profitable. One way to understand the importance of retention is to ask this question: if a brand acquires a new customer, what does that matter if that brand can’t keep the customer engaged? Prospecting without first nailing down the current customer makes teams spin their wheels and waste energy.

Align your performance indicators

So, we have our goals narrowed down and all teams are working towards a common purpose.  The next step is to flawlessly align our performance indicators to those stringently selected goals. Again, narrow your focus and be strict with the fidelity of indicators to goals.  They should have either a clear cause-and-effect relationship or a very strong correlation to prove success.

Once we’ve identified those core components, we can simply let the rest of the data wash away.  It takes work up front, but that work will be rewarded with a strong path forward and will avoid data paralysis down the road. By deriving indicators naturally from a core set of goals, you organically narrow the data set, so we can focus on producing insights that drive change.

It’s easy to see how many brands can get stuck in the mud during this phase. There are so many temptations, so many paths to take that could work if only for one added piece that we don’t have in the model. But this is a faulty mindset and the effort will be wasted with little to show for all that added work. Put the blinders on and be strict.

Where to start

The answer is almost always transactional data, especially if we’ve felt the impact of overwhelming data paralysis. Stick to transactional indicators early. They’re reliable and strongly aligned to behavior. What shows customer sentiment better: a Facebook Like or purchasing items?

Measure, rinse, repeat

Lastly, all of that work is useless if we don’t have a measurement plan in place to prove success. If we can’t measure, it doesn’t matter.

The best approach is a rigorous test-and-learn strategy. Not only does it prove success, but it also provides actionable insights for the future to help build individual successes into larger groups of changes across channels and teams to drive and achieve goals.

Analytics teams can definitely get backed up, especially with A/B testing. Sometimes the waitlist is daunting. But there are two good options if that happens. First, consider an outside agency dedicated to helping us learn about the customer. An outside source can provide focus when things get too tight for internal teams to produce results.

The other option is to test historically. I can hear the gasps and guffaws of analytics teams, but we need to read the tea leaves however we can to produce results. That means pushing changes to market and measuring year-over-year data instead of one-off direct causations.  That option is better suited to areas where we already know best practices or have some data points to suggest the right decisions with high degrees of confidence.

Another reason it’s a viable option — and why analysts should love it — is that it frees up the testing schedule dramatically. So many tests don’t really need to be run in an A/B format; sometimes we have years of historical data or mountains of best-practice to influence our decision. In those instances, measurement is less of a read and more of a confirmation.

Bring order to chaos

These ideas may sound simple and, to a large degree, they are simple. They’re foundational. But without a foundation, how can we achieve our brand aspirations?

So many brands run before they can walk and they fall flat. To bring order to chaos, we need to start with the lowest common denominators to build on our learnings. Start small, grow big. Incrementally and soon, teams from every channel will have the learnings they need to act and provide the best experiences possible for both the brands and the customers.

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