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In this age of big data, data shortage is not a problem. In reality, the opposite is the case. Retailers are frequently drowning in their own data, triggering an overwhelming sense of anxiety and uncertainty about their futures.

Additionally, these companies typically do not have access to the resources that can transform this mess of data into something accessible, actionable, and ultimately profitable.
In this article we will outline how to eliminate data overload and transform your marketing team into action-oriented database marketers.

Revitalize your customer portfolio

The general reason most companies’ databases are inadequate and incongruous is that their Point of Sale (POS) systems were developed to solely process transactions – not provide marketing intelligence.
POS data typically consists of five key data categories:

  • Who bought?
  • What product?
  • For how much?
  • When?
  • Through which channel?

While such information can be useful on its own, this is merely scratching the surface. The fact is, these rudimentary systems were not designed with a long-term customer relationship in mind.

For instance, not only do POS systems fail to track the trails leading from one purchase to another, they do not connect the dots of the different stimuli that incentive each subsequent purchase. They are also not directly actionable – rendering your data management abilities a far cry from their full potential.

Ingest/Filter you Customer Transaction Information

Marketing software solutions have evolved dramatically over the years – offering small and medium sized businesses capabilities they have never had before. A Predictive Marketing Automation (PMA) platform is one such class of software.

PMA’s apply machine learning and AI to solve marketing challenges and automate marketing campaigns with the goal of optimizing profitability over time. Unlike POS solutions, PMA’s are able to maintain customer databases, predict the relative value of customers, and automate marketing communications.

One of the chief goals of marketing is to know as much about your customers as possible.

In turn, the first priority is to completely vet your data hygiene by getting your POS data cleaned, filtered, and organized. This is done by uploading all of your records into a PMA’s standard data model.
Think of this as cleaning a messy garage, fine-tuning a car, filtering dirty water, assembling scattered puzzle pieces together, or weeding a garden.
Following ingestion through a standard model, all of your retail information is now stitched together across all available channels.

By simply uploading your customer transactional data into a PMA, you are already well on your way towards developing the comprehensive customer portraits, strategies, and services possessed by the Amazons and Walmarts of the world.

Without a holistic, 360-degree customer perspective, it is impossible to accurately calculate anyone’s customer journey or Customer Lifetime Value (CLTV). Inturn, your chances of succeeding - let alone competing - are incredibly slim.

Define and Create Reference Tables

For marketers to be truly effective, they need to understand all of the data collected about their customers. Cryptic codes need to be translated into English for analysis purposes.

Thus, reference tables are constructed from the various types of transaction codes used to track retail or online sales in POS systems. These typically include customer data, transaction data, and product data.

For example, if a buyer enters a store and purchases a jacket on clearance (where the price has been dramatically reduced), that particular item would have a special transaction code indicating the markdown in price.
This simple set of information can be used to form much clearer individual buyers profiles. For instance, if the person who bought the jacket also purchased other items on sale (either that same day or over a longer period of time) he/she could be categorized as a discount buyer.

Every nuance of each purchase (full-price, discount, coupon, time/date) that is captured by POS systems can be ingested into a PMA with full business descriptions provided by reference tables.


In other words, PMA’s ingest transaction codes and provide the magic “decoder ring” to make sense of the data.

Compile and Access Promotion History

The digital world we inhabit is a closed system in which most forms of online activity can be readily tracked and stored.

One of PMA’s most valuable elements is the ability to establish a promotion history facility. This systematically tracks and deduces how each customer responds to various forms of communication and /or solicitation (email, messaging, offers, etc). As a result, Customer Relationship Management (CRM) efforts can be managed more effectively.

Therefore, if your company sends out a promotional email to its customer base, a PMA can tell you which particular strides each recipient made towards a purchase. For instance:

  • Who opened the message?
  • Did they click through to the site?
  • What items did they browse?
  • How engaged is the customer with your brand overall?

Any of these bits of information can offer powerful clues about exactly where each of your customers is along their respective journeys and how to best manage your dialogue with them.

For instance, if a customer scrolled down to a product detail page (PDP) and exhibits dwell time beyond their average PDP dwell time, feature this product in a followup email over the next few days – even if it’s not the only content in the email. These smart little nudges are sometimes the final trigger to making the purchase. Doing this process at scale, across your website and customer base can have a material impact on your sales.

Marketing is all about relevance. Your chances of securing new customers or inciting repeat purchases are all about sending the right message, to the right person, at the right time, via the right channel.

Define Your Metrics

Another component of this process is defining the most important set of metrics that are specific to your business. Known as Key Performance Indicators (KPI’s), these are ways of measuring your customer management and overall company performance.
Different cohorts of customers should always be treated differently. Some metrics can be more valuable than others in different contexts – depending upon the types of the behaviours you are attempting to trigger. Therefore the most pertinent metrics should be defined, discussed, and consistently evaluated.

Customer Acquisition Cost (CAC)

A crucial metric to evaluate in this context is Customer Acquisition Cost (CAC). CAC is determined by dividing all of the costs spent on obtaining new customers by the actual amount acquired in a particular time frame.
Ultimately, the goal is to minimize CAC as much as possible by carefully assessing your Return on Investment (ROI) for each customer won.

Customer Lifetime Value (CLTV)

Customer Acquisition Cost should always be measured in tandem with another important metric, Customer Lifetime Value (CLTV), which determines how much a customer is worth to you over their lifetime. (We talk more about CLTV in this article: Customer Lifetime Value: What it is and Why it’s Important for your Business)
Customer value metrics can also be assessed in relation to different factors and time frames (1-year, 2-year, historical, etc).

Historical Value


Another simple metric involves examining how much money a customer has spent in the past over various time frames.
For instance, say that a frequent flier has flown 1 million km with an airline over his/her lifetime, but now no longer flies as much as he used to. Meanwhile, another customer has flown less overall (say 500,000km) but much more within the past year.
Despite the fact that the former has flown with the airline twice as much overall, he would likely not receive the same degree of perks as the latter. This is because the frequency and trend of recent travel typically lead to a projection of high future value, outweighing the overall number of miles over a longer period of time.

Projected Future Value


As discussed in our airline example, projected future value is another key metric that is often overlooked. If you blindly assume that every customer has the same future value, then you are likely missing out on a range of opportunities.
For instance, if you do not know what a target or cohort’s projected future value is, how would you be able to truly define your level of marketing success?
In turn, your company should have the ability to answer these key questions:

  1. Are we achieving the full expected value of each type of customer that we have in our database?
  2. How much should we invest in each customer down the road? Via which channels?

Always aim to recognize customers for what they’ve done in the past while also cultivating future behaviours.

The bottom line is that a PMA system can utilize both historical and current customer behaviour to project the expected value of customers in the future. Marketers can use the software to show their finance departments exactly how investments in their omnichannel campaigns are delivering the required ROI. This is an easy, accessible way for marketers to justify marketing expenditures.


By this point, your customer and transactional data have been uploaded into PMA software, where it has been systematically organized and filtered.
Not only do you now have a clean garage, but you also have a well-oiled, fully fueled vehicle ready to hit the road and meet your goals. But in order to get there, you need a roadmap identifying who your customers are and where to find them.