Conversion Optimization

eCommerce Customer Segmentation: 10 critical mistakes businesses make

Most retailers make the same customer segmentation mistakes that cost them sales. In this post, we tell you what they are and how to avoid them.

eCommerce Customer Segmentation: 10 critical mistakes businesses make

1 in 4 customers says they’d cease to do business with a brand if they’re not relevant—as per Accenture’s study.

Stats showing how customers choose brands that are relevant

As a result, brands lose as much as $1Trillion to competitors—simply because they’re not relevant enough!

Most retailers know the answer: customer segmentation. 

The problem isn’t that retailers aren't using customer segmentation. The problem is that they aren’t doing it enough. 

It’s not about budget constraints. Because companies allocate a fair enough share to marketing (11–13%). In fact, the eCommerce industry accounts for the highest percentage of average spend on advertising (21.9%). 

The problem is investing in the wrong areas that don’t offer a good return on investment (RoI). This is what Rakuten Marketing finds out in their research of 1000 marketers around the world: they waste about 26% of their marketing budgets on the wrong channels and strategies. 

In this post, we discuss the customer segmentation mistakes most retailers make and how you can avoid them to improve your revenue. 

Mistake #1: Not aligning segmentation with your overall business goals

The ultimate goal of marketing is to meet key business goals and segmenting without keeping that at the forefront often leads to missed opportunities. 

Whether your current key business goal is to turn more infrequent shoppers into loyalists, improve average cart value, reduce return to origin orders, or get more app downloads, your customer segmentation criteria should align with a particular goal and must be highly targeted to win over that segment of shoppers. 

For example, if your goal is to improve brand loyalty, your marketing campaigns around it must be targeted towards infrequent shoppers or buyers who are yet to explore your loyalty program. 

Similarly, if your goal is to get more app downloads, you must create a customer segment of predominantly mobile users and target your campaigns towards them. 

Mistake #2: Segmenting based on instinct and not data

Creating customer profiles based on their demographic attributes, likes, and preferences can surely go a long way in driving results for your marketing campaigns. 

However, you shouldn’t just stop there. Analyzing the results of your previous campaigns might pleasantly surprise you. The segments you presumed would engage or convert better might not actually do so and the ones you thought were low-value shoppers could turn out to deliver better ROI. 

For example, let’s assume that you are in the business of organic and gourmet food delivery. Your hypothesis is that adults within the age bracket of 30 to 40, working in tier 1 cities in large corporations would most likely be interested in your products since this segment is increasingly concerned about their health and looking to switch to healthier alternatives. 

You start targeting this segment aggressively. However, you don’t really receive impressive results, owing to your poorly segmented targeting customer list. What you missed out on was deeply analyzing the previously collected customer data. That’d have clearly told you that young adults—especially college-going ones—spent the most on your online store: a segment that you never thought could be a high-value buyer. 

Here’s a similar example from Avocode. It creates a highly specialized campaign for a niche target audience: young students who don’t have a huge budget. Plus they seal the deal with an irresistible discount offer. 

Example of segmentation based on age by Avocode

Had they tried to create a broad segmentation by including older professionals, the campaign wouldn’t have been as effective. 

Don’t waste your time or money relying on hunches, secondary research, instincts, or just some basic customer insights. Instead, spend time on readily available customer data and then move on to segment your lists. 

Mistake #3: Ignoring preferred communication channels

Not all your customers are super active on social media, not all of them prefer to connect with you via a call, not many read their emails every day, and not all are well versed with chatbots. 

The larger point here is that understanding the preferred channel of communication by your customers is an important criterion to segment them. This way, you’ll only reach out to your customers via their preferred channel and hence have a higher chance of engagement. 

For instance, you can use social ads for young shoppers who prefer engaging on social media, while emails for customers who have regularly opened and engaged with your previous emails. 

For buyers who have received satisfactory resolutions via a chatbot in the past, you can create engaging campaigns for them via chatbots. Use your previously collected data to create smart segments as per the channel preferences of customers. 

Referring to industry and your own customer data to dig out which channels customers prefer can help. For example, this research shows the preferred channels for brand communication among people from various age groups across the US. 

Chart showing referred communication channels for different age groups

Mistake #4: Not taking into account the time factor

If you operate globally and have customers from all corners of the world, triggering your campaigns at the same time for your entire list might not be the wisest idea. You need to ensure your email doesn’t land in your customers’ inboxes at 2 am. 

Therefore, make sure you segment your lists based on shoppers’ location and time zones so your campaigns are delivered only at the most optimum time and end up being extremely effective. 

Moreover, even if your target audience is from the same time zone, communicating with them at the same time could hurt your results. This is because what might be a good time for a working professional to engage with your brand, might be a terrible time for a student. 

For example, people working in corporate jobs like to randomly scroll through social media and engage with ads whenever they get a break in the middle of the day. But, for students, targeting ads in the first half of the day is futile as they might not even be near their device to engage with your brand. 

See this specific message sent out to customers in Ireland on a rainy day. It works only because it takes into account the person’s name, location, and the present situation he’s in. 

Example of customer segmentation based on timezone

Sending this at any other time may backfire. 

Therefore, the time factor for each of your customer segments is extremely crucial when designing and scheduling campaigns to ensure the best results. 

Mistake #5: Not segmenting based on current data

A lot can change over time. A high valued customer segment that drove high ROI for your campaigns 6 months ago, might not do so anymore. Similarly, a segment of shoppers that is extremely engaged with your brand on social media, might not be the same a few months down the line. 

The bottom line is that only the most recent data must be used for all your segmentation efforts to ensure they don’t go to waste. This is because as your times change, your brand evolves, and so do your customers, which impacts their buying preferences and interests, and therefore their reaction towards your marketing communication. Therefore, never rely on old data, no matter how promising, to create fresh target segments for your campaigns. 

For example, let’s assume that you run an online clothing store. Upon analyzing your customer data, you noticed that a certain category of product—say fleece jackets—were extremely popular among a target segment a couple of months ago. So you decided to run a targeted campaign specifically for this segment and this category of product. 

The results, however, turned out to be quite surprising: the same segment reacted rather poorly to the same product-related campaign. The reason could be simple—you took into consideration older data. The peak in product affinity could be justified by weather conditions that only last for a short period—something that went neglected while creating your campaign. 

Mistake #6: Segmenting based on limited data 

Many marketers fall into the trap of using very restricted and incomplete data to create customer segments, which very often turn out to be faulty. Google’s research fiend out that round 61% of marketers fail to get the data they need. 

They overlook collecting granular data that could yield specific insights and rely solely on surface-level attributes to feed into their campaigns. The results, therefore, turn out to be unexpected and rather disappointing simply because of incomplete data points. 

Instead, focus on gathering comprehensive customer data before you begin using it. For example, if you run an accessories store targeted towards teenagers—make sure you actually collect data regarding the age bracket of your shoppers. Without doing so, you can never be sure your campaigns are reaching the audience it is intended for. 

Also, such lists need to be updated regularly as this particular data point will keep changing. 

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Mistake #7: Segmenting too broadly

Keeping your segmentation criteria too broad will give rise to inconsistencies in the behavior of shoppers within the particular segment. There are likely to be multiple degrees of variations within one segment if your criteria is too generic or wide-ranging. You need to break down your criteria and make them more specific to drive more effective results. 

Broad segmentation leads to poor targeting and ineffective marketing campaigns that might be as good as no segmentation at all. 

For example, if you own an online grocery store, simply targeting everyone who has shopped online for food in the past 6 months might not make for a very intelligent segment as it is too broad and generic. 

You must break it down further by categories of products your target audience is interested in or the kind of interests they have or their location, etc. to give yourself focused customer segments that you can target and win over. 

Mistake #8: Segmenting too narrowly

Segmenting too narrowly, on the other hand, has multiple limitations as well. If your segmentation criteria is too niche, you will first need so much more data about each of your customers to evaluate them. 

Even if you manage to get that amount of data, keeping your criteria too granular will increase the number of segments to be created and the amount of data and results that will be gathered. Moreover, the information you drill down will be broken down to the least common denominator and not yield meaningful insights about your target segment as a whole. 

Instead of creating too many excessively specific segments, focus on creating a few valuable segments that derive many fruitful results. 

For example, let’s assume you are in the business of selling athleisure wear online and you want to segment your target audience. 

A niche segment such as shoppers who are interested in sports and fitness, have shopped from an athleisure brand online in the past, and care about fashionable clothing, might not be the best idea. This is because athleisure in itself is a niche concept and breaking your segments down so granularly will not give you a good enough sample size in each of the cohorts. 

Mistake #9: Not using clean data for segmentation

Data is the cornerstone of the customer segments you create. Erroneous, inaccurate, outdated, inconsistent, or duplicate data will outrightly harm your segments and lead to faulty campaign results. Therefore, before directly putting the customer data you collect to use, you need to clean and streamline it to ensure its accuracy and sanctity. 

Before you get to that, make sure you have a single source of truth for your customer data. This refers to one common database that you rely on for all your source data or multiple databases that converge into one, which forms your master data source. This will ensure data consistency to avoid any and all fallacies in your campaigns. 

Next, while sourcing data for your campaigns, make sure you use the most updated list, check it thoroughly for duplicates, errors, inaccuracies, and other forms of bad data, and spend time cleaning your lists. 

Mistake #10: Not understanding the difference and connection between segmentation and personalization

In times when context is everything, personalization and segmentation may seem alike. They both require understanding the audience or the person you’re addressing. But the difference lies in the depth of that knowledge and how you’re putting it to work. 

Segmentation allows you to divide your customers into logical groups based on their past behavior on your store, interests and likes, purchasing habits, or static attributes such as demographic and geographic ones. 

While personalization is a more granular concept wherein you study each and every customer and tweak and tailor your communication with them as per their specific preferences and interests. 

But the mistake doesn’t end at not knowing the difference. It is also the lack of understanding of how the two are a part of the same puzzle, that results in not being able to get the results most marketers expect from campaigns! 

Here's a visual depiction of how data collection, segmentation, and personalization form the stack of delivering a great customer experience. There is no personalization without thorough segmentation. 

pyramid showing relation between data collection, segmentation, and personalization

Creating powerful customer segments like a pro! 

As long as you steer clear from the above-mentioned mistakes while creating customer segments, you are all set to run well-targeted campaigns that will yield promising results. Apart from that, don't shy away from experimenting with data-backed hypotheses to confirm what works and what doesn't with each of your high-value segments.

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