By AdExchanger Guest Columnist
While many retail marketers say they are data-driven and analytically informed, many never connect critical insights and marketing decisions.
In fact, just 53% of marketing decisions are data-driven, prompting 60% of marketing CMOs to decrease their marketing analytics departments because of “failed promise improvements.” At the end of the day, just 17% of B2C marketers believe their data-driven marketing strategies are helping them achieve their goals.
This is unsurprising.
Marketers are managing a deluge of customer insights, including structured, unstructured and semi-structured data. This firehose of information can be difficult to harness during the decision-making process. Social media, in particular, can cloud the waters.

Likes and followers are not without merit. They have their place in measuring brand health. They reflect positive goodwill toward the brand and show customer engagement at some level. However, in many instances, this engagement is not directly causal of sales rates. So what should marketers be considering instead? The answer isn’t one thing. Rather, it’s a process.
The metrics worth measuring 
Converting prospects from anonymous to known, then converting known prospects to customers, is the marketing end game. 
In the digital world, metrics that are actually predictive of this process include: time on page, content downloads, multiple clicks on products, site visits through various campaigns, intensity of chatbot engagement, requests to transfer from a chatbot to a human and providing email or SMS contact information. Recency, frequency and monetary value are also proven and historically reliable conversion predictors.
Customer data platforms (CDPs) provide the progressive profile that can reveal the connection between many of these metrics. This profile can then be used to take personalization to the next level, ultimately getting marketers closer to conversion. 
By tracking actual behavior, such as transactions, channel usage and other behaviors, the marketer can begin to personalize using advanced analytics to predict sales rather than rely on customer-supplied intent data, which can be unreliable. The graphic below is a sample of some items that are typically contained in a retail marketing CDP.

For example, a serious buyer looking for hiking boots may like or follow the brand, but this activity is not predictive of sales in a statistically meaningful way. Meanwhile, a high-potential buyer will click on banners, lightboxes, register or open an account, spend time on the brand’s website reading/downloading product collateral, etc. This behavior, when viewed in the aggregate and analyzed, is significantly more predictive of a sale than vanity metrics.
Making data matter
Data is an essential component of successful retail marketing campaigns. However, it is not enough to simply collect data. Retail marketers must connect critical insights with marketing decisions and focus on the events in the customer journey that are most causal and predictive of sales conversion.
By building a progressive profile for personalization and targeted one-to-one offers, marketers can move customers from a member of an audience or segment to a tailored messaging strategy based on the construction of this progressive profile. 
Ultimately, success lies in making data matter by using strategy-driven data science to deconstruct the customer journey and allocate marketing budgets to the events that are most likely to indicate upcoming purchases. 
“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
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