How to build the perfect customer … profile
How would you describe the perfect customer? To me, the ideal customer loves your product and sees the value in the goods or services you provide – and isn’t shy about sharing that opinion with others. He or she is engaged with and responsive to your new products and offerings, and doesn’t shop exclusively from the sale section. This perfect customer is also known as a customer advocate.
In case you don’t recognize the term, a quick refresher: as we’ve covered before, advocates fit into the fourth stage of the customer lifecycle, which define shoppers as they progress through stages of considering, purchasing, engaging, and subsequently either disengaging or becoming brand advocates. Advocates are the people who have become loyal customers, who not only like the product and continue to purchase, but also tell their friends and family about the brand.
It is easy to see why it’s so important to work on nurturing shoppers to the advocate stage – and to keep them coming back. The most precise way to achieve this, as many online retailers are now doing, is to collect and analyze big data and predictively build customer profiles. Doing so will help identify the potential high-value customers or those about to churn, and approaching them with personalized, relevant, well-timed offers via lifecycle marketing campaigns will keep them purchasing.
When building customer profiles, segmentation is key. By dividing customers who share certain characteristics and behaviors into different groups, you will be able to send them relevant, tailored messaging to increase your customer retention rates. Here are important data points to consider when segmenting your customers:
1. Demographic Data
One of the easiest ways to treat your customers differently is knowing their basic attributes, such as age, gender, household income, profession, or geolocation. Sending batch-and-blast marketing messages is like selling snow supplies to users who live in tropical climates. Target customers in relevant professions that can utilize your products or services to their advantage.
2. Behavioral data
Your customers are constantly providing you with data about their interests, preferences and behavior via on-site, email, social, and even off-site everyday. You can imagine the endless amount of data populating here by the second. Identify what increases engagement by looking at time spent on pages, number of clicks and views, last touch points for exit pages, and referral sources. Not all customers abandon your store or purchase for the same reason. Figure what funnels, frequencies, and timing groups tend to convert the highest then craft your campaigns around that information.
3. Transactional Data
Using purchase history can help identify your best and worst customers. Bucket customers into groups such as 0x buyers, 1x, 2x, or 3x+ for high and low probability purchasers. Reward your high-valued customers by offering better incentives to keep them returning. Also, find the items and categories that are most frequently purchased and engage other customers who may have similar interests. Using this data, upsell and cross-sell to improve customer lifetime value and retention.
4. Source and Social Data
Understanding your customers’ referral source and influence is important as well. This not only improves your retention, but also acquisition conclusions. Know whether your customers come from organic, social, or affiliate channels. Look at how social influence can help engage your customers on-site and off-site.
In summary, there are many ways to segment your customer data. Start with the easily accessible categories and move onto advanced segmentations as you progress. Strategy-wise, be sure to try all of these customer data points, without overwhelming your tests. Take a few variables and isolate your segments for higher confidence in the results. It takes patience and time to successfully build a high-value customer profile, like painting an intricate picture stroke by stroke. Once finished, customer profiles will help predict who has the potential to become that advocate customer we’re all looking for.