Next Generation Database Marketing Defined

Consumer expectations for personalization are evolving to the point where direct marketing campaigns with a one-size-fits-all approach run the risk of alienating loyal customers. A customer who regularly buys a brand’s button-down shirts might be turned off if that same brand sends a catalog with nothing but golf shirts or offers a 20 percent discount on an item the customer just purchased.

This evolution in personalization is felt most acutely by brands utilizing traditional database marketing. In today’s marketplace, personalization through database marketing means adopting a segment-of-one approach across direct and digital marketing programs.

What is Database Marketing (Definition)?

The database marketing definition is a form of direct marketing that uses insights gathered from customer data to drive contextually relevant and personalized customer experiences. Customer names, phone numbers, email addresses and transaction history are examples of customer data that are collected in the database. The more information that can be added into the database and cross-referenced the deeper the understanding of the customer journey and personalization recommendations are possible.

Typically focused on customers that “opt-in” to mailing lists, database marketing has been used to reach customers that have already expressed interest in a company or a product. Brands would then use database marketing to try to influence future purchases and interactions. Its intent is to reach people who are most interested in a type of message or offer, but as the consumer has matured, so has the need for growth in how we define database marketing.

Challenges to Traditional Database Marketing

While data is the key ingredient to drive personalized interactions with customers with forward-leaning database marketing, many organizations struggle with the innovative use of data to create a seamless customer experience. The challenge stems from being unable to access the right data at the right time and with analyzing the data to produce actionable insights. The struggle creates a gap between database marketing strategy and execution that mirrors the gap between the expectations a customer has for personalized interactions and the actual experience that is being delivered.

One of the main reasons for this gap is many marketers have yet to integrate processes and infrastructure along all customer touchpoints. A marketing system that generates discount offers on an e-commerce platform may not be tied to data living in other systems. A lack of visibility into all data in a bi-directional way prevents companies from maximizing the use of data to generate insights, which drive personalized messaging, customized offers, and effective segmentation strategies.

Here are some of the key challenges companies face in implementing effective database marketing:

• Lack of Integration: Fragmentation and siloed storage of customer information hampers delivery of a seamless customer experience, but for many organizations a certain degree of fragmentation is difficult to avoid. An enterprise often has dozens of customer engagement touchpoints, with applications and systems highly optimized to run processes that serve a specific business purpose. This creates friction for a customer who chooses to interact with a brand at various touchpoints, which is increasingly becoming the norm. Integration of customer data across all systems in a bi-directional manner will help ensure a single point of control for all data related to a customer’s profile and the customer’s buying journeys.
• Analytics: Increasing data volumes means that generating insights to drive personalized customer interactions at scale is becoming more challenging. Human beings simple are incapable of presenting timely, relevant offers to thousands or millions of valued customers, or customizing an offer based on hundreds of previous interactions potentially dating back years. Database marketing in the age of big data requires advanced technologies such as artificial intelligence (AI) and machine learning that can mimic human behavior and mine unprecedented volumes of structured and unstructured data to apply insights at scale.
• Personalization: Data quality is a familiar challenge to marketers, compounded by data volumes and different data types that are inundating various systems. Data duplication and data matching concerns are nothing new, but a heightened value on personalization changes the equation. Today, data quality challenges also speak to identity resolution. Sending identical mail offers to the same record is a cost issue, whereas sending one win-back offer and another loyalty reward offer has a far greater chance of also annoying your customer.

The Role of Database Marketing in Segment-of-One Marketing

With a data-driven, segment-of-one marketing strategy, a brand can strike the right chord with relevant messages and offers that meet the customer where they are on their personal customer journey. It’s about moving at the speed of the customer, which goes beyond just knowing a customer’s transaction history. Does the customer shop on a mobile device? What is the customer’s in-store shopping patterns? What is the average number of engagement touchpoints before purchase? A brand armed with data can proactively shape the customer experience by providing timely, relevant offers and individualized direct marketing outreaches that drive loyalty and revenue.

This trend toward hyper-personalized marketing outreach is driven by data of all types and from multiple sources and tends to be unstructured. Traditional database marketing was limited in its reliance on structured data. A customer database that tracks individuals differently across account codes, name, address, and email and can’t correlate that data will fall short in generating an individualized marketing plan that is relevant to the customer.

Traditionally, a brand would categorize next-door neighbors in the same bucket based on shared identifiers such as geo-location, socioeconomic status, age, gender, and whether they’re saving for college tuition. Once in the same bucket they would spin up direct marketing campaigns accordingly, without a thought to which action would optimize the outcome. Next-generation database marketing runs analytics on structured and unstructured data to generate deeper insights. Perhaps one neighbor relies on a virtual assistant to be guided through a menu of services and features, while the other consistently prefers self-service options. An offer management strategy based on customer differences rather than similarities will go a long way toward optimizing the customer experience.

Segment-of-One in Action

One study revealed that 91 percent of consumers are more likely to shop with brands who recognize, remember, and provide relevant offers and recommendations. With more and more data being collected, marketers have a unique opportunity to transform their database marketing strategy to better meet customer expectations. Here are a few examples of next-level database marketing that recognize the value that increasing data volumes can bring to communications with customers in a segment-of-one approach:

• A CPG beverage company aggregated more than 300 external attributes of customers to deliver highly relevant customer experiences where and when the impact would have the highest engagement.
• A North American specialty retailer and distributor created a 360-degree customer view of its nearly 50 million customers to overhaul its loyalty program, individualizing communications for maximum relevance to deepen customer engagement.

A Single View of the Customer and Next-Level Database Marketing

Effective and cost-efficient database marketing begins with creating a single, 360-degree customer view that brings together data in real time across multiple systems. By knowing all that is knowable about a customer – the how, what, when, where, and why of their purchasing habits – organizations can personalize engagement with relevance, creating individualized marketing initiatives with the proper context and cadence. With this view, an organization can close the gap between strategy and execution and more closely align customer engagement models with the expectations their customers have for personalization.

Brands are beginning to compete based on customer experience. A recent CMO Council study of nearly 200 senior-level marketers found that more than half (55 percent) reported that they are implementing systems to extend marketing’s view of the customer to include insights from all interaction points along the customer journey. This is the race to break through customer data siloes to create a single view of the customer and enable a single point of control over the data, decisions, and interactions that make every moment of engagement with customers personal.

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