The Big Marketing Reset: Using CDMP to Propel Precision Marketing

predictive-analytics-9001

It should come as no surprise to anyone that the Covid-19 had a significant impact on eCommerce. According to a recent deep dive into online consumer behavior during the pandemic, between March and August 2020, one in five consumers switched from their regular brands, and seven in ten tried new digital shopping channels that they had never used prior to the shutdown. And while a wealth of consumer knowledge exists in those statistics, most retailers found they had to leave all that juicy customer data on the table. Outdated data capturing and modeling systems, as well as a lack of ability to interpret what such rapidly shifting behaviors symbolized, meant that many brand CMO’s had no other option but to ignore the valuable insight into their customers this data could have provided. On a deeper level, lack of confidence in their ability to apply the data in a transactional way meant little was learned or utilized from this unprecedented growth. While some marketers opted to revert to mass communication autopilot, savvier forecasters decided instead to lean in by modernizing their data collection and analytic modeling systems and adopting a true Customer Data Marketing Platform (CDMP) strategy, one that allows them true scalability and agility in an increasingly data-driven world. 

Precision Marketing: Data Propelled by CDMP

Deploying big data and using it to inform precision marketing efforts is about more than just capturing data; it’s about putting it to use, and ensuring it is shared across your entire ecosystem. Brands who began freeing their data from departmental silos and using it to drive marketing have seen the value of their efforts in real-time. Take the classic British fashion house of Burberry, who have been using big data and CDMP strategies to enhance performance, sales, and customer satisfaction across all their brand channels.

Using their customer loyalty and rewards programs as customer communication tools, Burberry continually asks their loyal users to share data with them and gives them something in return for that privilege. By gathering the data of past purchases from their brand as well as other responses to styles and preferences, Burberry makes relevant recommendations for their customers for both online and in-store products. Even more compelling is how this data translates into their brick-and-mortar storefronts. Sales assistants in the stores utilize tablets that provide additional information about the specific customer, such as their purchasing history, preferences, and even social media and coupon activity, along with purchasing suggestions.

Burberry continues to utilize data long after the customer has left the store. Using data gleaned from both RFID tags and QR codes embedded in the items the customer looked at or tried on while in the store, the customer receives customized information on where the products come from, what they’re made of, and get tips on how to style the item with other clothing items they have previously purchased. Additionally, this information is provided to them at a time predicted by analytic models of their past buying behavior. It’s an incredibly clever use of data and AI, and it’s a clear demonstration of how a personalized, data-driven interaction can boost sales; Burberry has seen in-store sales increase by 23% and online sales grow by 29% since they started doubling down on CDMP focused marketing.

How to Make CDMP Marketing Work for You

The continued focus on data-powered customization and analytic modeling can have some brands wondering how far down the AI rabbit hole they need to travel to see results realized in ROI. Here’s some good places to start:

Tap Into New (and Better) Data

Precision marketing and CDMP focused analytics are only as good as the data behind it. New models with old data will provide inaccurate results. To strengthen your insights, smart marketing strategists need to begin a new data venture with a wide-angle approach to data collection.Use behavioral trends and location-based insights as well as third-party analytics on your business, customers, and competitors to inform your current customer data. Companies starting this journey find the most value in incorporating data derived from third-party providers or CDMP experts like Group FiO into their models. Companies that extend their data can identify upticks in demand and where new customers are originating from, as well as determine which customers in their existing base have increased their spending, where your lapsed customers have gone, and why.

Invest in Tech that Learns at Scale

A fast and furious eCommerce world requires marketers to get better at testing and faster at reacting. The keyword here is AGILITY; you need to adopt a more agile operating model that learns at scale. This requires developing technology capabilities that considers aspects like signals of consumer intent and responses to marketing messages, and then feed them back into the marketing engine so it can learn what works and what doesn’t. Marketers who are adopting true artificial intelligence (AI) to monitor campaigns and responses at a detailed level are learning not only what works and what doesn’t for which segments, at what times, and over which channels, and then using that data to develop a nimble marketing strategy based on those insights. Trying to accomplish these feats using standard analytics would take the average marketing organization several days, but by using an AI-enabled CDMP, it’s something that can be accomplished in minutes.

Lean into Remote Strategy

Agile practices are effective in allowing marketing teams to test consumer behaviors and react quickly to changes. While traditionally that meant a team who was working in the same “war room” to deploy marketing strategies quickly, the pandemic has demonstrated that remote teams and third-party data strategists can perform at the same level when their data is good, and their modeling is even better. Leading companies are converting physical war rooms into virtual ones and using collaborative tools to help manage CDMP rollout. The best companies have gone a step further by integrating some of their vendor teams into their remote practices and using shared tools and compatibility guidelines to account for vendors’ different technologies.

By capturing new data, searching for new behavioral relationships, and enabling rapid experimentation, marketers can seize granular growth opportunities and utilize data-driven practices with greater resilience and ROI.

If you’re looking for a way to begin your own reset and boost ROI, Book a Demo today!