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What is attribution modeling?



Understanding the steps a customer takes before converting can be just as valuable to marketers as the sale itself. Attribution models are used to assign credit to touchpoints in the customer journey.

For example, if a consumer bought an item after clicking on an display ad, it’s easy enough to credit that entire sale to that one display ad. But what if a consumer took a more complicated route to purchase? She might have initially clicked on the company’s display, then clicked on a social ad a week later, downloaded the company app, then visited the website from an organic search listing and and converted in-store using a coupon in the mobile app. These days, that’s a relatively simple path to conversion.

Attribution aims to help marketers get a better picture of when and how various marketing channels play contribute to conversion events. That information can then be used to inform future budget allocations.

Attribution models

Following are several of the most common attribution models.

  • Last-click attribution. With this model, all the credit goes to the customer’s last touchpoint before converting. This one-touch model doesn’t take into consideration any other engagements the user may with the company’s marketing efforts leading up to that last engagement.
  • First-click attribution. The other one-touch model, first-click attribution, gives 100 percent of the credit to the first action the customer took on their conversion journey. It ignores any subsequent engagements the customer may have had with other marketing efforts before converting.
  • Linear attribution. This multi-touch attribution model gives equal credit to each touchpoint along the user’s path.
  • Time decay attribution. This model gives the touchpoints that occured closer to the time of the conversion more credit than touchpoints further back in time. The closer in time to the event, the more credit a touchpoint receives.
  • U-shaped attribution. The first and last engagement get the most credit and the rest is assigned equally to the touchpoints that occured in between. In Google Analytics, the first and last engagements are each given 40 percent of the credit and the other 20 percent is distributed equally across the middle interactions.

Algorithmic, or data-driven attribution. When attribution is handled algorithmically, there is no pre-determined set of rules for assigning credits as there is with each of the models listed above. It uses machine learning to analyze each touchpoint and create an attribution model based on that data. Vendors don’t typically share what their algorithms take into consideration when modeling and weighting touchpoints, which means the results can vary by provider. Google’s data-driven attribution is just one example of algorithmic attribution modeling.

Custom attribution. As the name suggests, with a custom option, you can create your own attribution model that uses your own set of rules for assigning credit to touchpoints on the conversion path.

Benefits, limitations of attribution

So is that it? Pick a model and be done? Not quite.

Marketers face the ongoing challenge of being able to stitch all the various touchpoints available to their customers together for a grand view of attribution. There have been improvements, with greater ability to incorporate mobile usage, in-store visits and telephone calls into models, but perfection is elusive.

“Given the increasing fragmentation of platforms and the types of media that marketers have available to them, attribution has never been more important from a marketing measurement perspective,” says Simon Poulton, senior director of digital intelligence at digital marketing agency Wpromote. “Unfortunately, the nature of attribution is one where the goal posts are constantly being moved and just like an asymptote, we’ll never be able to reach the point of 100 percent attribution.”

Chris Mechanic, CEO and co-founder of digital agency Webmechanix, agreed. “Any attribution model is going to be messy,” Mechanic said. “Find one that makes some degree of sense and stick with it. Whether it’s first touch, last touch or blended, the really important thing is getting everybody [on a team] to buy into it and then stick with that over time.”

As marketers invest in more channels and digital mediums, getting a unified view of a customer’s journey is only getting harder. “This will become ever more complicated by increased investments in influencer marketing and Amazon where there are significant challenges in creating unified IDs,” Poulton said. “While many of the major players like Visual IQ and Neustar look to solve for some of these challenges via partnerships, we will still be faced with larger challenges on the horizon for out-of-home (OOH) media as one example.”

“In addition to the customer journey tracking that (Google’s and Facebook’s attribution platforms) provide, we’ll likely see the development of variance analysis solutions within the platforms that will enable marketers to better understand the existing impact of their strategies,” Poulton said. “At an overarching level, the key takeaway here is the convergence of data across platforms and the ability to understand interactions that occur across channels in both an impression and click capacity.”

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Data & Analytics

When it comes to customer analytics, you need to be all-in



One would be hard-pressed to find anyone in a company that didn’t believe that customers are critical to success. But at the same time, you might find that the same customer-focused people don’t really know much about their own customers in the first place, or that their understanding is limited to their specific domain. In a data-rich world, this lack of holistic insight is not only unforgivable – it’s unprofitable.

Go big or go home

McKinsey published a study a few years ago looking at the value of customer analytics. At the time, they raised concern around the fact that the perceived value of customer analytics was “declining, rather than growing.” Paradoxically, companies that relied heavily on customer analytics were twice as likely to perform better when measured on metrics ranging from profit and sales to ROI.

The catch? It was the heavy users of customer analytics, those that deployed tools extensively through their organization that reaped the benefits. Just dabbling in the analytics or deploying tools to a few departments was akin to not even trying. It seems that there is a minimum threshold, or perhaps critical mass before the effect of customer analytics can be felt. Also, the research discovered that having tools alone was not enough. A data-driven culture that can incorporate these insights is necessary to benefit from analytics in the first place.

What does fully-committed really mean?

Obviously, companies work hard to ensure that their departments have the customer information they need to do their job. For example, a customer service rep is taught to deal with irate customers who have issues with their products. Marketers build up customer models often populated by tracking and monitoring web activity in the hope of uncovering purchase intent. And so on.

There is a subtle problem here. In the same way that companies can’t just dabble in customer analytics, they also can’t afford to create isolated pockets of customer analytics within each department. For starters, each team will be limited by the customer data that it collects. Even the advanced data analytics capabilities of modern marketing tools are effectively limited to web data that they can collect from potential customers interacting with blogs, advertisements and emails.

To truly develop a holistic customer model, companies must integrate many different data sources. A richer model should at a minimum include data the following sources:

  • Sales (CRM)
  • Billing
  • Finance
  • Marketing (social, blogs, campaigns, etc.)
  • Customer support
  • Operations

By separating customers into fully distinct groups that rely on multi-dimensional considerations, a company can define unique strategies to target these customers. For example, it makes sense to go after high-revenue customers. Layering in profitability might split these high-revenue customers into high-profitability and low-profitability groups. Layering in sales funnel data to the high-revenue / high-profitability group might further expose customers that take a long time to close vs. those that close within weeks. And so on… The point here is that the addition of a new data source from each department leads to a much more useful understanding of the customer that can in turn be deployed across all teams. This is the essence of being fully-committed to customer analytics.

What next?

To really benefit from customer analytics, first develop a holistic model of your customer that integrates data from as many sources as possible. But insight alone is not enough. Each department within your company must be willing and capable to act on this insight. This involves leadership in incubating and maintaining a data-driven culture. It also involves efforts to ensure that you have appropriate data infrastructure in place to get insights to your employees when they need it.

It’s not easy – without a full commitment to customer analytics you risk wasted effort. But the rewards are worth it.

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Data & Analytics

Mind the GDPR Generational Gap!




A new study by data specialists Wilmington Millennium reveals that Millennials and Generation Z are the most empowered age groups when it comes to protecting their personal information. Sixteen to thirty-four year olds are the most likely to act on the powers afforded to them by GDPR.

Almost half (48 per cent) of Generation Y and Z have taken some action since GDPR was introduced last May, including requesting their personal information is deleted by an organisation, finding out what personal data is held on them by an organisation or contacting the Information Commissioner’s Office (ICO) to make a complaint. By comparison only a quarter of Generation X and a third of Boomers have taken similar steps.

Millennials are most likely to ask for their information to be deleted, with one in three saying that they have already done this. This rises to one in five for the rest of the population. Generation Z are the most likely to both request a data audit (15 per cent compared to an average of nine per cent) and complain to the ICO with 18 per cent saying they had contacted the Information Commissioner to register a data breach or data processing concern. This compares to just 7.5 per cent for the remainder of the population.

Boomers were the least likely to take any action with only one per cent saying that post GDPR they had contacted the ICO, three per cent claiming that they had contacted a business to find out what information is held on them and 15 per cent requesting that their information was removed from a marketing database.

Comments Karen Pritchard, Director, Wilmington Millennium:

“It is interesting that it is the younger generations that are actively protecting their personal information, rather than the older age groups who have been campaigning for greater control over their data rights for years. The discrepancy between the ages groups is significant – for instance 18 per cent of Generation X versus one per cent of Boomers making a complaint to the ICO. Despite this, it shows that GDPR is having a positive impact with consumers becoming increasingly data savvy. This is a good thing as our research shows that the majority of people now believe that marketing communications are better than they were prior to the 25thMay 2018.”

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Data & Analytics

Marketers react to Gartner finding: Martech spending now exceeds staff costs



Marketers are now spending more on marketing technology than on salaries for internal staff.

That’s a key finding in the recent Gartner CMO Spend Survey 2018-2019, which surveyed UK- and U.S.-based senior marketing executives.

Marketers spent 29 percent of their budgets (not including media spend or agency fees) on martech last year, while allocating 24 percent of their resources to paying staff. In 2017, the percentages stood at 22 and 27 percent, respectively.

‘No surprise’

The main beneficiaries of this spending were digital analytics, content management and email marketing tools, making martech what Gartner called the “single largest area of investment when it comes to marketing resources and programs.” The top three marketing capabilities cited in the survey were marketing/customer analytics, marketing technology acquisition and use, and customer experience.

“Marketing leads are focused on building their analytics and martech capabilities because they’re the muscle groups that need the most development,” HubSpot VP and MarTech Conference program chair Scott Brinker posted about the report. But, he added, the whole purpose of this development is to move toward the objectives of customer acquisition and retention.

“It’s no surprise,” Acquia CMO Lynne Capozzi told me via email. She noted that a study by her company, which serves companies using open source content management software Drupal, found that 62 percent of global marketers plan to spend more on martech over the next 12 months, in part to simplify the current complexity of connecting systems and data to deliver good customer experiences.

‘Tremendous race to understand data’

Phil Ahad, EVP of Strategy and Products at online survey provider Toluna, said that enterprises — such as those favored in the Gartner report — have “a lot more room to reduce costs” of staff salaries through marketing technology than do smaller companies, which are leaner.

“I’m not at all surprised” at the Gartner finding, David Frankel, managing partner of sales and marketing consultancy Slingstone Group, told me. He’s the former CMO of financial data firm Edgar Online and of alternative lending company Tapify.

Both from his personal experience and from observing the marketplace, he said, it’s clear there’s a “tremendous race to understand the data around customer experience.”

In 2011 and 2012, he said, marketers started talking about Big Data, but efforts were mostly focused on harnessing and structuring it.

“People didn’t fully understand what you could do with the data,” he added, noting that we’ve since begun to focus on using data to support customer experience through the targeting of individuals with messages, best offers and streamlined processes.

‘A game of catch up’

Now, there is a greater understanding among marketers that they need analytical and implementation tools to improve experience through a better understanding of customer data, he said.

“It’s a game of catch up” based around data, he added.

Frankel said that he didn’t believe this increased spending on martech will fall off over the next year. “We’re seeing a new status quo,” he said, especially since AI needs a growing infrastructure to capture, clean, manage and feed quality data, and since SaaS services involve ongoing subscriptions.

Bayer VP of Media Strategy and Platforms Josh Palau agreed that this emphasis on martech spending “is probably where the industry is going,” in large part because many marketing services are purchased in the cloud, through software as a service offerings.

In his previous stint at Johnson & Johnson, he recalled, obtaining a social listening service subscription added a substantial capacity that required very little commitment in terms of internal staffing. Palau said “maybe two people” were involved in using the subscription at the brand.

Much of his internal staff these days is focused on strategy and management, he noted, since cloud-based services are used to meet needs that once would have required significant internal personnel. Even though Bayer is working to bring all digital media in house, he said, there will still be an ongoing need for the outside cloud-based services.

“It’s a lot easier to change platforms than to change staff,” he pointed out.

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

The post Marketers react to Gartner finding: Martech spending now exceeds staff costs appeared first on Marketing Land.

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