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Can Retailers Benefit from Using Multi-Touch Attribution?

How ecommerce marketers can benefit from using multi-touch attribution

By Ryan Koonce

E-commerce, driven by multiple new channels, is a booming business. “The NRF expects that online retail will grow 8-12%, up to three times higher than the growth rate of the wider industry, racking up nearly $445 billion in sales.” But today’s consumers no longer purchase in a silo; they read customer reviews on social media, attempt to price match on Amazon before ever heading in-store, and visit brand websites to understand product details. Buyers visit a site an average of 9.5 times before making a purchase. More than ever, the consumer purchase journey is evolving into an ever-expanding range of online and offline channels and interactions. As these hyper-connected consumers increasingly rely on a variety of digital and physical resources when making purchase decisions, how then do ecommerce marketers effectively engage them AND get more get ROI from their media spending?

Ecommerce marketers are adapting their strategies to deliver the right ads with the right offers at the right time and through the right channels and devices

Marketers know that the shopping landscape is changing drastically with the rise of new channels, content, and connected devices. As reported in Ad Age:

  • Shoppers will use smartphones in over one-third of total U.S. retail sales in 2018 for researching, comparing prices and purchasing.
  • 88% of consumers research their purchases online before buying, whether it’s online or in-store.
  • 65% compare the in-store price with the online price while in the store.
    Shoppers who use multiple channels to conduct product research spend 14% more than single-channel shoppers.

Hyper-connected consumers expect a highly personalized experience from all their interactions including organic search, retargeting ads, paid search, remarketing email and/or direct mail offers. They expect you to know them, show them you know them, and help them at every touchpoint. Poor performing campaigns will flop with shoppers, wasting dollars and costing you lost sales. As a result, marketers are rapidly converging channels, content / offers, and devices along the consumers’ purchase journey, where having the ability to track, measure, and optimize each touchpoint has become the secret weapon. Yet many marketers continue to use antiquated attribution models, like last touch, to determine how they optimize their touchpoints to maximize sales and ROI.

But last or single-touch attribution is ineffective for an omni-channel / content / device world

Single-touch attribution consists of first touch and last touch models, giving the full credit of the sale to the touchpoint a consumer interacted with first or last such as a promotional email or a display ad. Any interactions before or after that, clicking a Facebook or paid search ad, for example, are not credited with the sale. According to retail marketing strategist Mike Farrell, attributing a sale wholly to the last (or first) interaction before the purchase is outdated and inefficient. The customer journey has grown increasingly complex, so optimizing for a single action or channel ignores a much bigger picture of how consumers engage with your touchpoints on their way to purchase. A customer, for example, may discover a new product on Pinterest, rediscover that product through display ad retargeting, research the product via Amazon, and finally make the purchase after receiving a promotional email and/or direct mail. This journey can occur across desktop and mobile devices.

Valuing only the last touch ignores the very important role each marketing activity had in driving the sale and limits marketers’ ability to develop greater cross-channel cohesion. That’s because you can overly emphasize top or bottom-funnel activities, neglecting touches across the full purchase journey. Ecommerce marketers need to know the effectiveness of each marketing touchpoint in every consumer journey regardless of where those touchpoints occur. Without the ability to track this and tie interactions across channels, content, and devices to one user, it’s virtually impossible to know which marketing initiatives are working and which ones aren’t.
Envelope and exclamation point
Last-touch attribution is inadequate for tracking today’s real-world touchpoints and hyper-connected journey, resulting in missed revenue and misspent dollars.

Multi-touch attribution connects real-world touchpoints and devices to consumer journeys and provides a single version of return on spend

By reconciling all available touchpoints to a unique consumer identity, multi-touch attribution helps marketers see precisely how each consumer moves through the purchasing funnel and reach them on their preferred channels. The granular detail allows them to better understand how customers interact with their media, more accurately attribute credit to each interaction, and more effectively optimize their media buying. Since this insight is produced in near real-time, marketers can quickly capitalize on opportunities to improve engagement and influence purchase decisions at each stage in the consumer journey.

Once linked, identities previously attached to multiple devices are collapsed into a single identity. This cross-device attribution gives marketers a view of the end-to-end consumer journey. By linking touchpoints and devices to a single person and journey, marketers can clearly see when they are serving too many ads to the same person or when two or more display vendors are overlapping each other serving ads across the same domain. Multi-touch attribution can help marketers understand exactly which factors are driving shoppers to convert and fine-tune their tactics to influence their decision to buy and deliver a successful experience that wins over new customers, and keeps existing ones coming back for more.

Multi-touch attribution uses different models to meet your needs

While single-touch attribution only gives credit to one touchpoint, multi-touch attribution models assume all touchpoints play some role in influencing a sale. Some popular models are:

Linear multi-touch attribution model is the baseline for multi-touch attribution models. It assigns an equal percentage of revenue credit to each touch regardless of its recency in the buying journey.

Time-decay multi-touch attribution model assigns revenue credit to each touch based in accordance to its recency in the buying journey. The closer the touch was to the sale or conversion event, the more influential it was.

Position-based (or U or W-shaped) multi-touch attribution model gives 40% revenue credit to the first and last touch, with the remaining 20% applied equally among the rest of the touches. A modern multi-touch attribution platform allows you to configure these percentages.

Data-driven machine learning multi-touch attribution model uses historical touch and conversion data with machine learning to derive a custom algorithm to assign revenue credits to touches.

There’s no perfect science for choosing the right multi-touch attribution model. As the use of multi-touch attribution technology evolves, marketers will run several models concurrently to see which one is right for their business. The good news is modern multi-touch attribution tools allow you to see and compare the results from each model.

Multi-touch attribution uses cohort analytics to give you deep insight to optimize acquisition channels and campaigns.

Cohort analysis the most effective ways to gather information about your channels and campaigns. A cohort method quantifies the ROAS of your channels and campaigns based on a cohort (date or date range) and captures the spend and stream of conversions to report profit, break-even and even lifetime value. In multi-touch attribution, your cohorts may look something like this:

How much revenue did we generate today? this week or month?

How long does it take for my ad spend to break-even?

How are we trending by channel? which ones are consistently delivering top ROAS?

Modern cohort analytics allow you adjust cohorts (without spreadsheets) to any date or date range and easily visualize ad performance, discover patterns, and optimize channels and campaigns in real-time to quickly optimize your channels and campaigns.

Multi-touch attribution keeps your advertising channels honest about their ROAS

Because advertising platforms each use their own pixel to track ad performance, your conversion counts can easily become overstated. For example, let’s say Molly is shopping for a laptop and clicks on your Google search ad on Sunday; clicks on your AdRoll retargeting ad on Tuesday; and on Friday she clicks on your Outbrain native ad which takes her to your site where she purchases a laptop for $600. In this purchase scenario, Google, AdRoll and Outbrain reports show 3 unique conversions totaling $1,800, but your ecommerce system only shows 1 conversion to 1 buyer at $600.

Multi-touch attribution automatically connects campaign details from Google Ads, AdRoll, Outbrain, and other advertising platforms to the consumer purchase journey and allocates a portion of the revenue credit (total not to exceed 100%) to each touch in accordance to their role in influencing the purchase. This built-in reconciliation eliminates the over-counting problem associated with using conversion data from each platform to guide your ad spending. Having this true ROAS insight (along with cohort analytics) also allows you accurately compare the performance of each advertising platform to what’s really converting (or not) and confidently rebalance your total spend to maximize revenue and return.

Multi-touch attribution works hand in hand with your ecommerce platform

Multi-touch attribution doesn’t replace your ecommerce platform – it complements and enhances it. It enables ecommerce platforms such as Shopify and WooCommerce to do what they do best: automate and scale back-end functions and consumer-facing activities and transactions for your storefront. Concurrently, multi-touch attribution focuses solely on quantifying which marketing touches, campaigns, and channels are working (or not) to help ecommerce marketers make better decisions across the entire purchase journey.

Multi-touch attribution also complements your ecommerce platform by integrating synergistically and standardizing the attribution method to see an unbiased impact of every customer interaction from search keywords to retargeting ads to direct mail offers. It enhances workflow with existing tools and channels and eliminates the chaos and inefficiency of extracting and normalizing channel-specific analytics. It also provides the data that fuels effective budgeting and optimization by helping marketers to know where to place their best bets to drive efficient revenue growth.

Ecommerce marketers should plan for multi-touch attribution.

Consumer expectations for relevant and personalized experiences have soared. Winning their business means understanding their unique preferences and behaviors, as well as the marketing messages and offers that influence their decision to buy. With a true understanding of their customers and the tactics that influence their decision to buy, marketers can deliver a successful omnichannel / device experience that wins over new customers and keeps existing ones coming back for more.

While it may seem daunting, moving away from last touch or first touch attribution models will quickly become an imperative for retailers. As consumer journeys have grown in complexity, marketers need to ensure their attribution models keep up. With multi-touch attribution, ecommerce marketers can understand consumer interactions across different channels and devices, spend smarter than their competitors, and improve consumers’ overall shopping experience to encourage conversions.

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What is Multi-Touch Attribution? How do I Choose the Right Model?

Choosing the right multi-touch attribution model for your marketing

By Ryan Koonce

What is multi-touch attribution marketing?

Multi-touch attribution is the practice of assigning credit to marketing touchpoints in proportion to their impact in driving a desired conversion outcome. The operative term here is “proportion.” Multi-touch attribution models collect data from all touches in the buyer’s journey and assign proportionate credit based on the attribution weightings of a model, thereby accurately reflecting their impact (or not) in generating a sale.

For example, if a prospect or visitor comes to your website through a search ad, converts to a lead on a newsletter, engages in person with a sales person, and revisits your website through a remarketing ad to make a purchase for $100, multi-touch attribution (using a linear model for illustrative purposes) would assign $25 in proportionate revenue credit to each of the four touches. And depending on your view of the impact of the recency of the touch, multi-touch attribution models come in basic flavors to easily fit your strategy.

Unlike multi-channel attribution, multi-touch attribution can track any marketing touch with a deep level of granularity, such as online channels (any source, medium, campaign, etc.), events (any sales touch), email campaigns, and even direct mail, mobile devices and television.

What problem does multi-touch attribution marketing solve?

Remember the earlier word, “proportionate”? The problem is single-touch attribution allocates a disproportionate amount of revenue credit to either the first or last touch. If your customers’ purchase path is straightforward, single touch attribution is an adequate method of informing budget allocation using tools like Google Analytics, CRM or ecommerce. However, like most modern B2B and B2C businesses, you’re probably marketing through multiple paid platforms and marketing channels including offline, single-touch attribution misrepresents ROI by giving 100% revenue credit to either the first or last, woefully understating the impact of other touches and channels.

For example, if a prospect or visitor comes to your website through a search ad, converts to a lead on a newsletter, engages in person with a sales person, and revisits your website through a remarketing ad to make a purchase for $100, multi-touch attribution (using a linear model for illustrative purposes) would assign $15 in proportionate revenue credit to each of the six touches. And depending on your view of the impact of the recency of the touch, multi-touch attribution models come in basic flavors to easily fit your strategy.

Linear multitouch attribution model

Further, single-touch attribution data from ad-specific ad sources such as Google Analytics and Facebook Insights often double-count revenue with each source claiming 100% revenue credit to a sale. With this “attribution anarchy,” marketers can’t make data-informed decisions as to which channels and campaigns are worth spending more dollars on and which ones are wasting dollars.

What are the different multi-touch attribution models? Which one is right for my marketing?

While single-touch attribution only gives credit to one marketing touchpoint, multi-touch attribution models assume all touchpoints play some role in driving a conversion such as a lead, pipeline or a sale. Here we’ll look at the most popular multi-touch attribution models and how you can choose the one that’s best for your marketing.

Linear multi-touch attribution model

A linear or “impartial” model is the baseline for multi-touch attribution models. It assigns an equal percentage of revenue credit to each touch regardless of its recency in the buying journey. Hence, using the earlier example, each touchpoint receives an equal $15 in revenue credit, totaling $100.

If you don’t have a strong view of the value of when the touch took place in the buying journey, then a linear model is an excellent starting point. Don’t try to boil the ocean! The good news is with a modern multi-touch attribution tool you can easily toggle between attribution models to see the differences and start learning.

Time-decay multi-touch attribution model

A time-decay model assigns revenue credit to each touch based in accordance to its recency in the buying journey. Whereas a linear model gives equal credit, a time-decay multi-touch attribution model says the closer the touch was to the sale or conversion event, the more influential it was. Using the same buyer journey, you can see the more recent touches receive more credit than the older ones.

time decay multitouch attribution model

If you believe strongly (and have some supporting data) that more credit should be allocated to touches that nudged prospect closer to a sale vs touches that brought the lead or buyer in, then a time-decay multi-touch attribution model is the way to go.

Position-based (or U or W-shaped) multi-touch attribution model

A position-based (or U or W-shaped) model assigns 40% revenue credit to the first and last touch prior to the conversion event or sale, with the remaining 20% distributed equally among the middle touches. If there is only one touch before a conversion event or sale, then a position-based multi-touch attribution model would give 100% revenue credit to the touch. Similarly, if there is only a first and last touch, they each receive 50%.

Let’s look at our example to see how these models work:
position based multitouch attribution model

Note: A W-shaped model works the same as a U-shaped model except it allocates 30% revenue credit each to the first, key middle, and last touch, while distributing equally the remaining 10% among other middle touches

If your business places more value on the touch that brought in the initial prospect and the touch that converted them compared to the middle of the buyer journey touches, then you should use a position-based multi-touch attribution model to optimize your campaigns and channels.

Data-driven machine learning multi-touch attribution model

Custom machine learning modeling is the most advanced approach to multi-touch attribution. A custom model uses a machine learning algorithm to assign revenue credits to touches. Rather than using a static model with user-determined weightings as in the above examples, an algorithm is derived using historical touch and conversion data. As you can see in the example below, data-driven modeling applies a unique algorithm to allocate credits.

Custom Machine Learning multi-touch attribution model

It is for this reason you should not expend the effort with it until after you’ve gone through at least one or two full path buying cycles using one or more of the static models. If you’re business is direct-to-consumer with a short purchase path, this means a matter of a few weeks, buy if you’re dealing with longer B2B buyer journeys I would learn from the static models for a good six or more months depending on your sales cycle to determine if there is ample opportunity to further optimize your marketing mix.

There’s no perfect science for choosing the right multi-touch attribution model. As the use of multi-touch attribution to optimize budgets and channels matures, marketers will run several models at one time to see which one is right for their business. The good news is modern multi-touch attribution tools allow you to see and compare the results from each model.

How do I know I am ready for multi-touch attribution marketing?

Regardless of whether you’re a B2B or B2C marketer, multi-touch attribution is worth the effort if you’re on the hook for lead or pipeline generation and/or customer acquisition or purchase, AND you’re:

  1. Using multiple digital ad networks such as Google, LinkedIn, AdRoll, Facebook, and others. As the number of ad channels grow, single-touch attribution crumbles under the tracking complexity. With less linear and more complicated customer purchase paths, multi-touch attribution effectively manages the attribution intricacies of different ad channels (and the products and metrics within each channel required) for informing budget and revenue allocation across the different ad platforms.
  2. Using or considering using offline channels such as direct mail, conferences, stores and/or sales touches, AND want a consistent way of attributing and comparing revenue and cost credits. The minute you do offline marketing is the moment single-touch attribution tools such as Google Analytics become useless in giving you holistic and accurate visibility of the ROI of your online and offline channels. Multi-touch attribution is channel-neutral, thereby allowing you to accurately compare ‘apples to apples’ ROI between online advertising and event spending, as well as the insight within each offline channel.
  3. Using or considering using SEO to spur brand awareness and qualified traffic to your site, AND you want a consistent way of attributing and comparing revenue and cost credits from this channel to others. Here only multi-touch attribution can connect anonymous touches from your blogs and web pages, and accurately assign revenue and cost using the same model (and conversion data) as your other channels.
  4. Spending more than $250,000 annually to drive B2C sales or $500,000 annually to generate qualified B2B leads or pipeline. This is considered a general inflection point where your marketing efforts are increasingly better spent on knowing which channels and campaigns are working and which ones need to be purged for poor ROI vs getting mired in attribution chaos due to lack of true attribution insight.
  5. Starting to boil over the frustration associated with trying to optimize budgets using single-touch attribution from one or more tools, exceeding the effort to switch to a multi-touch attribution., which is why you’ll want to look at the change as a journey using the above guidelines when considering the different attribution models.

    If you have a short and simple marketing funnel, a single-touch model may be good enough. But if you’re marketing on various channels, have many touchpoints and are nearing “attribution anarchy” with single-touch tracking, you’re ready to move to multi-touch attribution marketing. Now.

    Multi-touch attribution marketing is both a technology and culture

    Because buyer journeys span many touchpoints and devices before converting, marketers need to understand which touchpoints a buyer (or buyers in the case of B2B account-based marketing) interacted with that resulted in a positive action. The goal is to understand where to focus spend, devoting funds to higher performing channels and campaigns and diverting dollars from those that were ineffective, making the change to multi-touch attribution marketing is as much a culture shift as it is a new technology. With the click of a mouse, it enables marketers to look at all channels and attribute the sale to those touches using various models. Marketers can look at user-level data (clicks, forms) their touches have on key conversion goals such as leads, signups and purchases. Unlike single-touch attribution, multi-touch attribution models enable marketers to better understand both the chronology and the type of interactions that preceded and influenced conversions, using the insight to optimize the conversion paths of buyers.

    Get started with multi-touch attribution