In marketing pursuits the demographic of one is the ultimate destination as it achieves the highest response rate. That is, each marketing campaign message is precisely targeted to the needs, buy criteria or maximum receptivity of a single customer.
But as marketers and chief customer officers know all too well, one to one marketing strategies seldom reach the 'demographic of one' because of expense and complexity. However, Customer Relationship Management software with its integrated marketing automation and systemic data analysis make one-to-one marketing at scale a reality.
This goal requires leveraging unique combinations of data from your CRM system and other sources to define precision campaigns directed to high fit target audiences and delivering relevant, contextual messaging.
The reality behind the demographic of one is that the more closely you tailor your campaign to your target audience profile, interests and purchase journey, the more likely you will produce the desired response. Consider the following five steps in your pursuit to a demographic of one.
Harvest Your Data
By its nature, CRM software tends to produce a lot of data. This provides a valuable repository to sift and mine for customer variables, segmentation, personas, trends, contextual insights and patterns.
One-to-one marketing requires knowing enough about your customer's desires, behaviors and motivations that you can deliver highly relevant, timely and personalized messages or offers—and grow your customer relationship over time.
CRM systems are great for tracking historical business transactions for each customer, but that's not enough to really understand and predict what your customer wants. Fortunately, customers are becoming social, and volunteering their interests and motivations in social networks, blogs, tweets and other social channels. By capturing this (normally unstructured) data and appending it to your CRM system contact records, you can glean valuable customer information, and achieve real customer insight, that can fuel successful one-to-one marketing strategies and campaigns.
This is, in effect, low level data mining, without the need for data warehouses and expensive business intelligence (BI) programs.
In addition to the obvious customer demographic factors such as age and income, less obvious searches through the data can produce highly useful variables. If you are a retailer, for example, you can compare the zip codes of your customers with the locations of your stores. Some businesses are highly location centric. That is, they draw most of their customers from their immediate neighborhoods. Many others draw more uniformly across the geographic area. With this information you can know whether to concentrate your marketing on nearby neighborhoods or to cast your net more broadly.
Time of year is another example. It's obvious that a ski shop will do most of its business in the early winter. It's perhaps less obvious that an auto tune up and repair business is likely to experience a winter surge as well.
When searching for customer behavior patterns, it's important to realize that the process tends to be iterative. That is, first you sort your data by criterion A and then re-sort by A and B, and so on. Often the gold will be buried five or six layers deep.
Segment Audiences Based on Dynamic Variables
In effect, marketing data analytics is a scientific experiment. In subdividing your audience you are generating hypotheses about what motivates them to act and then applying the data to validate or disprove your idea.
You may want to start by test marketing your approach to a sample set within individual categories and measuring the responses. You want to collect information on the effectiveness of your campaigns and compare them to your hypothesis. If the results of a particular marketing campaign are disappointing, compare the campaign results to other campaigns that applied some of the same variables, such as target audience, offer, content, channel, or timing, and see which correlate.
Sometimes you will prove your customer hypothesis completely wrong. A classic example of this happened to Mobil Oil a few years ago. Mobil developed a campaign for its premium gasoline based on the perfectly reasonable notion that premium fuel was purchased by more affluent customers. Unfortunately the campaign failed.
Based on the analysis, the company discovered that the buyers of premium gas were anything but affluent. They were for the most part older blue collar consumers who were driving older cars that required premium fuel for their high compression engines, or younger consumers who wanted better fuel for their hot rods. With that information, Mobil refocused its campaign.
A careful study of the characteristics of customers in the CRM database can help you prevent these kinds of costly mistakes. For example, comparing the ZIP codes of premium fuel purchases with the known demographics of those zip codes can give you a rough idea of the economic status of the customers. However analysis of results is still indispensable and testing is essential.
Develop Highly Targeted Messaging
The next step is to develop the relevant, personalized and contextual messaging and incentives for your micro-demographics. Messaging must be aligned to the characteristics of your target audience persona or finely tuned customer segment and include content or incentives that move the recipient to a predicted action.
Potential economic value of the target group plays into this process. A group made up of customers who regularly make large purchases is obviously more valuable – and worth a larger incentive – than irregular purchasers or customers who don't purchase as much.
Analyze and Adapt
Use the reporting and analysis features in your CRM software to track and analyze the results of your campaigns over time. CRM apps with purpose-built marketing analytics and AI, such as the two Salesforce marketing clouds, are especially helpful.
This is a critical step in any marketing campaign but it is especially important for highly targeted ones. Precision campaigns will produce significantly more positive responses, but will also produce more near misses. Analyzing the near misses will provide an immediate and significant pickup in a follow-on distribution.
Remember that the ultimate purpose of the exercise is a series of profitable marketing campaigns which resonate with recipients because they are so closely aligned with their interests. Keep in mind that this kind of marketing is also an exercise in balancing the costs of analysis and the number of campaigns against the returns. That said, with each successive effort the cost declines and the conversions increase, thereby driving a significant and sustained improvement to marketing ROI.