If you’re in marketing, most likely you’re concerned about attribution. Marketing attribution is the process of evaluating all the marketing touchpoints that lead to a purchase, and determining which touchpoints have the most influence on a customer’s decision to take action.
Doing attribution the right way is important for a couple of reasons: 1) prove ROI of marketing spend to upper management and demonstrate their contribution to the revenue pipeline, and 2) it helps determine how to best spend their future budget for maximum impact.
There are many models available for calculating attribution, most of which assign value to marketing campaigns through statistical analysis at the user-level. Typically, an attribution model focuses on single touch, which gives the greatest value to either the first or last customer interaction; or multi-touch, which distributes the value to each interaction throughout the buyer’s journey. Google Analytics’ default attribution model, for example, is a last non-direct click model, which means whatever channel a user touches on their last, non-direct interaction before converting is assigned 100% attribution. Google offers more advanced models, as well, including multi-touch models.
Multi-touch models tend to be more accurate, as the buyer’s journey is rarely linear. A consumer may see an ad on a social media channel that prompts a website visit. While on the website, he may download a datasheet. After reading the datasheet, he may see another ad offering a discount -- the nudge he needs to make the purchase. All of the attribution shouldn’t go to the last ad, though, because the three other touchpoints served to educate the consumer and pique his interest. Other factors may have influenced his decision as well, such as his perception of the brand or his physical location.
The Drawbacks of Today’s Statistical Attribution Models
Simple Statistical models for assigning attribution have become popular among marketers in data-driven organizations, giving them more insight into which marketing activities provide the most bang for the buck. However, they can be inaccurate. Several pitfalls of statistical attribution methodologies can cause biases that skew results. Here are a few:
Correlation Bias: In-market and cheap inventory bias -- sometimes referred to as “cookie bombing” -- are common in correlation-based attribution algorithms and can lead to inaccurate measurements. In-market bias refers to the practice of giving too much credit to an ad or other customer touchpoint when the prospect is already in the market or interested in purchasing. Cheap inventory bias makes lower-cost media appear to be more impactful than it is, as the targeted customers were already in the process of converting.
Digital signal bias: Some attribution models fail to factor in offline activities and how they lead to online sales. For example, say a retailer sells most of its products at brick-and-mortar locations, but determines digital media spend based on website activity. Such a model doesn’t take into account the omnichannel experience during the buyer’s journey.
Brand perception bias: Statistical attribution models tend to discount the impact of a brand’s reputation on consumer behavior. It’s important to consider the interplay between brand perception and conversions in addition to the persuasiveness of any touchpoint or interaction leading to a sale.
User-level messaging bias: Marketers can mistakenly deem an ad with a particular message ineffective, when in reality, it would be very effective with a specific, smaller target audience. This is where analyzing attributions at the person-level is critical.
Location bias: For location-based businesses, advertising may be more effective for attracting customers that are close by. Not understanding the impact of location and other environmental data can create gaps in your understanding of what’s working and what’s not, causing attribution errors.
The cookie conundrum: Cookies are often used to analyze consumer behavior, but internet users may delete cookies, and cookies expire after 30 days. So, a recent ad may appear more effective than an older one, even if it wasn’t, simply because the cookie for the older ad has disappeared.
Because of the many pitfalls of statistical attribution models, smart marketers are turning to AI.
AI-backed Attribution Models Are Highly Accurate and Improve Over Time
Using statistical methods for complex marketing analytics to determine lead scoring and attribution don’t model complex consumer behavior accurately -- but AI models do. And, leading marketing teams know it. In fact, according to Adobe, top-performing companies are more than twice as likely to use AI for marketing (28% vs. 12%).
AI models that leverage machine learning can be used to analyze the impact of various touchpoints and help you understand which activities – or what combinations of activities – are most likely to steer lead conversion. They can be customized at a granular level, taking into consideration the particular characteristics of your audience, offerings and messaging, and trained over time to become increasingly accurate with the input of new data. In this way, they can accommodate changing customer behaviors and preferences that typical attribution models would not take into account. They can also enable organizations to make predictions about how consumers will respond to certain marketing activities and messaging. Here are some key benefits of using a highly customized AI model for marketing attribution:
Optimize marketing spend: Knowing what activities, content and messages have the most impact guides budget allocation decisions and ensures you avoid wasting dollars on things that don’t work. With AI, you can factor in many more parameters, such as location data, brand awareness and perception, past purchasing behavior and more, to further target your audience and refine your message.
Better forecasting: AI models can be trained to provide increasingly accurate insights over time, enabling you to predict what marketing activities will be most effective and develop extremely accurate forecasts. This increases the chances you’ll hit pipeline goals and other KPIs with consistency.
Highly personalized content: Instead of throwing ads and content out there and seeing what sticks, you’ll have the specific insights you need to develop relevant, meaningful content and messaging before you send it to your audience. And, your audience will be more targeted to begin with, so the chances of conversion are much higher.
Increased ROI: With highly customized, relevant ads and content delivered to targeted audiences, you’ll convert more leads into customers -- and you’ll have the attribution data to demonstrate the value of your spend.
Better products: Armed with person-level attribution data, companies gain insight into customer needs and preferences, which can inform product roadmaps and updates, leading to increased customer satisfaction.
Drive Revenue with AI-powered Attribution
Marketing has become an extremely data-driven discipline, and with good reason. Today’s consumers are increasingly skeptical and discerning, because they’re bombarded with so many messages and so much content on a daily basis. Yours will be ineffective or ignored, unless it hits the mark. Only AI-powered attribution models can provide the level of insight needed to deliver highly targeted, relevant messages to the right potential buyer, through the right channel, at the right time.
Learn how 2predict can help you build and refine an attribution model that helps drive lead conversions, marketing impact, and your ROI.