Personalization platforms are a new must-have for all businesses, but sometimes they look like a Christmas tree covered with decorations. To help everyone who has experienced this pain, here is a how-to guide for choosing a retail personalization platform.
In this article, I will share my opinion about why marketing personalization has become so popular, what principles should be followed when choosing a personalization platform, and what a typical decision tree might look like.
Nobody likes spam and unnecessary offers. However, spam, robotexts, and robocalls will increase dramatically in 2023. If a store makes marketing at least a bit more human by remembering previous purchases and personalized messages, the customers’ attitude towards it changes. This approach is often called “human2human marketing” or “marketing with a human face.” This means: do not spam; do not pressure a buyer; help them make an informed choice based on personalization.
Below is the data I’ve collected on the added revenue from personalized campaigns among Mindbox clients. The dark green bar is the lowest added revenue among the companies in a particular industry. The light green bar is the highest one.
For example, newsletter personalization can add 3-16% in revenue among retailers selling home goods and furniture.
When brands realized the potential of personalization, the marketing tech sector exploded.
The number of martech companies increased from 150 in 2011 to 9.9 thousand in 2022.
There are now so many viable technologies to pick from, each with its own functions and merits.
Marketing personalization (automation) technology is a complex tool. Be ready for the fact that the preparation and selection of a platform will take longer than implementation.
Here are my top five tips for successful implementation:
The goal of platform implementation is the most important. If your team cannot agree on a clearly defined goal, you’ll never reach success. So it’s better to postpone the purchase of automation technology until everyone is aligned. Strong goals include success metrics, timelines, and a chain of responsibility that everyone agrees on.
For example, the team implements a website personalization technology and makes a statement about success: Conversion has increased by 0.5%. What is this measured against? Is this a large or small amount?
This metric is easy to inflate: improve the campaign design and the number of emails sent. Sometimes this is precisely what is hidden behind flashy terms like “growth hacking.” You will receive more orders attributed to the email channel using this metric.
The reason for the apparent growth in this case is traffic redistribution. Customers will often visit the website using a link from an email with a new design rather than typing a URL or using a search engine.
Real-life example: A chain of canteens inside business centers has implemented a loyalty program. Both metrics show excellent growth, but the company’s turnover and revenue are not increasing. Why is this happening?
The problem is that no new customers have appeared in the canteens since people from the outside simply cannot get there. The same employees from the same business centers continue to go to the canteens that they used to go to before. Only now, the audience has been divided into layers—people who spent more money registered in the loyalty program first. As a result, both metrics look rather good.
The number of program participants is growing, and the average check per participant is higher than for ordinary customers. However, there are no real benefits for the business. Moreover, there are drawbacks. The marginality of sales fell because regular customers began to buy at a discount.
The examples below qualify as good measurements because it’s easy to quantify the results and challenging to inflate or misrepresent them.
United Colors of Benetton used this metric and looked at segment creation, increase in conversions and average order value to measure progress. INCANTO looked at automated recommendations based on consumer behavior to track this.
Reducing the company’s workload is the primary thing that affects this metric, as shown in the cases of Burger King and Olant. Another factor that impacts these results is reducing total marketing costs, see United Colors of Benetton.
Here, the use of control groups and the number of new monthly campaigns are the two main factors that can influence this goal. Flower delivery service Blossom Flower Delivery leveraged both of these to get real, quantifiable data to improve their processes.
 
Such goals in practice can mean that the team or a particular marketer does not really understand what to do.
Are you familiar with the situation in the table below?
This is a classic situation when a large company tries to compare several technologies. Sometimes completely different ones. A giant table is created, sometimes containing several hundred lines, where different departments enter their functionality “wish list.”
Unfortunately, the final decision about which platform to use is often left to the people who are either too busy or unqualified. As a result, they fill out comparative tables based on shallow information like basic presentations or site descriptions. Sometimes they even ask contractors to fill out the tables for them.
However, even in a best-case scenario where everyone is qualified, does great research and creates a high-quality table, there’s still a high probability that it won’t lead to a good choice.
Imagine you are an advanced marketer: You want to build RFM segments and launch targeted campaigns and promotions based on them. This requires technology, so you decide to do a side-by-side comparison of platforms that each perform very different overall functions:
When you look at the line “Ability to send campaigns by RFM segments,” all three are marked “yes.” Thus, at first glance, it seems the question of choice comes down to price.
However, there’s a problem: What did they really mean by “Yes?”
Therefore, you need to decide whether a particular technology suits you based on an assessment of use cases and not on functionality comparisons.
Workflow 1: Our programmers developed a system that counts RFM. I just want to email address lists corresponding to the desired segments.
Solution: An inexpensive email gateway is better suited for the workflow.
Workflow 2: I want to try making targeted campaigns for RFM segments, but I don’t really understand what effect it will give or how it should be done, and I definitely won’t do it by myself.
Solution: You need an agency or a niche service that will quickly and cheaply do everything for you ‒ with a certain degree of transparency.
Workflow 3: I regularly recalculate and edit RFMs. The segment is used for email, SMS, mobile push notifications, and promotions. Each time a new marketing mechanic is launched takes a lot of time from analysts and programmers; it just doesn’t make sense!
Solution: An automation platform is needed here.
Ask potential candidate platforms to do the following:
I have also prepared a list of essential things to consider when choosing an automation platform.
Companies that sell their products across online and offline outlets or use several different channels of communication with their customers face a problem called “data integration.”
Customer information is spread piecemeal across several systems and databases, often with considerable overlap.
Buyers’ personal data, behavioral data, purchase history, and promotional points are stored in any or all of the following locations:
First, you have to cleanse and unify customer data. This process should consider possible errors and typos in the full name, cities, and addresses, and the confirmation of contact information (email and phone number). It must also be secure so that when combining the two records, access to the account with the loyalty program bonus points does not get into another person’s hands.
Next, relevant information from different systems should be collected, classified, and merged around this “cleansed” record, e.g., what product a person clicked on in the email, what they looked at on the website, what they bought in the end, how much it cost, how they used bonus points, and so on.
Ideally, when changing data for a specific person, it is necessary to recalculate the segments they were previously placed in and use common sense when changing communications tactics. For example, after combining duplicates in the database, two supposedly different customers who made a single purchase may now be a single loyal customer with a much higher average order value. This is good news for you, but you do not want to immediately send them an email with promotions and discounts on expensive goods if they received completely different offers to one of their accounts before that.
All of this should work in real-time on large amounts of data (hundreds of millions of events for an average retailer), and it should be possible to manage external systems such as gateways or website personalization technologies.
Data cleansing and unification (and its sequences for business) is a complex thing from a technical, marketing, and business point of view. Because of this, data integration and systems often don’t receive the attention needed. Instead, businesses simultaneously buy and launch separate niche personalization services, which leads to chaos.
Do you regularly face the following situations?
All these are different manifestations of “integration chaos.”
The second, less obvious consequence of ignoring the importance of data is how it affects the usefulness of the algorithms, big data, and neural networks that so many rely on. Algorithms and big data are susceptible to data quality. If you feed them with fragmented and unclean data, the results will be fragmented, messy, and unusable.
The highest efficiency of automated product recommendations, for example, from Amazon, is associated with a large array of uniform, clean, and reliable data that the retailer has collected for many years.
Data integration is the cornerstone of meaningful and effective personal marketing. Without it, there is no point in investing in personalization tools – the result will likely be random.
The problem of effective data integration is so essential that a separate class of platforms to solve this problem was created: the CDP, or customer data platform. In the United States, the market for such systems is one of the fastest-growing, with the CDP Institute forecasting CDP industry revenue will reach $2.3 billion in 2023.
Therefore, if you are:
Be sure to take time to evaluate the CDP functionality on the platforms you are going to choose. Information on how to consider your options can be taken from this file.
As for interesting resources that talk about CDP (and where there is a directory of platforms, for example), I recommend cdpinstitute.org.
One of the relatively new trends in this industry is the emergence of integrated Customer Engagement Platforms (such as Mindbox). Platforms solve not only the problem of centralizing data, but also include marketing engagement tools. Such a products can significantly reduce the cost of marketing technology and accelerate the marketing team’s productivity by streamlining time-to-market hypotheses and new workflows.
Time-to-market (ТТМ) is a metric of the speed o f implementation and the “ease” of managing changes in the solution.
One of the largest companies in Eastern Europe uses a German enterprise marketing management system. The transactional email the brand’s online store sends after placing an order is simple text, very primitive, and without any kind of personalization. As we now know, the reason for this is not poor marketing.
To personalize this email, the platform needs to be improved. However, modifications can only be purchased from a system integrator company. The approximate work estimate is $44,000 with a six-month lead time.
Time-to-market changes in the example above are not acceptable.
Unfortunately, this is a classic situation with implementing enterprise solutions according to a technical assignment. One person made the decision, the technical assignment was written by someone else, and a third person implemented these decisions.
Ultimately, specialists who have to use the implementation results find themselves alone with a clumsy monster that has lagged behind the real state of affairs for a couple of years and an integrator company that builds its business on change requests.
One of the most critical aspects of personalized marketing is the ability to adapt quickly.  Fashion retailer 12 STOREEZ provides an excellent example of this approach by frequently conducting A/B tests to improve engagement and key metrics. For instance, one test revealed that addressing customers by name in push notifications did not enhance their response rates, contrary to the team’s prior assumptions.
Marketers should be free to take independent action without relying too much on IT, analysts, approval processes, or work plans. This enables them to pivot quickly and seize opportunities as they arise.
Let’s go back to the RFM segment example and try to understand how it can influence the choice in terms of TTM changes.
So, we have completed all of the previous steps: We formulated a goal and expectations for it,  developed a preliminary scenario and chosen a suitable platform (including CDP) with an acceptable TTM for implementation and changes. We now have to assess ROI.
I believe it is risky to implement technologies with integration time frames of six months or longer before any chance of seeing benefits or with a return horizon of more than a year.
This is because:
One of our clients, a mid-sized retail company, has been trying to implement a marketing automation platform for the second year in a row.
The direct and indirect switching costs start from $500,000 to a million or more.
From an outside perspective, this is a symptom of an insufficiently developed implementation solution.
On the other hand, it may well turn out that there is a plan. For example, to use the platform to increase site conversion by 2%. Then, the economy of implementation will most likely look very good.
A company that completed all the steps listed above is Blossom Flower:
Step-by-step scheme for decision-making and technology choice
Choosing the right personalization platform for retail marketing can be a daunting task. With so many technologies available and each one offering different features and benefits, it can be challenging to determine which one is the best for your business.
However, by following the right principles and taking the necessary steps to prepare for implementation, you can ensure a successful outcome. This includes setting clear implementation goals and metrics, defining use cases for the technology, integrating and cleansing data, evaluating the speed of implementation and changes, and estimating the ROI.
Ultimately, personalization can greatly benefit your business. Provide proper marketing that doesn’t spam or pressure buyers, increasing revenue, customer loyalty, engagement, and satisfaction.
Featured Image Credit: Photo by Canva Studio; Pexels; Thank you!
An experienced entrepreneur (formerly surgeon), investor, and data-driven B2C marketing evangelist. Executive MBA in Bayes Business School in City, University of London. The last company I founded has grown to the leading marketing automation CDP solution in the EU – Mindbox, trusted by hundreds of companies, including L’Oréal, Panasonic, and more.

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