In this four-part series, we look at the role data can play to improve your understanding of your business, customers, marketing and interaction strategy. Think of it like a first date with your data – you may learn something new or it may be revisiting existing knowledge, but without a first date you won’t get a second.
Part two: Curling up with customer data
If your business happens to have a customer loyalty system that tracks purchases and actions in a customer record, this next part is going to be easy.
Most loyalty systems include customer identification information – name, address, contact number, email and maybe date of birth, job type, communication preferences and social media IDs. Associated to that record will be a purchase history that lists anything bought when the loyalty card was presented.
Use the identification information to start building general profiles or personas of your customers. Personas allow you to give a segment of customers a face and a personality. Using the persona you can hypothesise the reaction of each segment to your business plans or marketing campaigns. You might see an opportunity for a new style of marketing campaign for a specific segment that you hadn’t considered before.
For this analysis, you don’t need to keep each record identifiable, just extract the demographic insights and then delete the name details – For example: if you use a title field, Ms, Mr or Mrs transpose into Female, Male or Female respectively. If you don’t use a title field, you’ll have to take a best guess on gender from the first name.
Start building the persona by summarising the data on separate fields to see what stands out – take each field and think about what it might tell you about the person, for example: address provides location (via postcode or suburb) and might also indicate dwelling type (an apartment address – 3/14 George Street – looks different to a house address – 14 George Street) this information might be relevant or might not depending on your business. Date of birth is valuable as it not only provides age cohorts, it also allows you to tap into generational research and generalisations to quickly fill out a persona.
Now examine purchase history.
If the same product appears multiple times, is there a similar timeframe between purchases – bought monthly, every six weeks, weekly? Do all purchases happen in the same stores (are customers loyal to a store or to the brand? Or is it just that the store is convenient?) Do purchasing habits change when a campaign is active? Do they buy more of the same or something new? Does a campaign change a regular purchase pattern and then the regular pattern resumes a while later? (it could mean price discounts are pulling forward future purchases at the lower price, indicating the campaign did little to stimulate demand, just moved the purchase date forward) What’s the combination of products bought in one transaction or over three months? Are there any patterns emerging?
Using the patterns and trends in the data develop personas for the dominant types of customers. This gives you a view of what customers did but not the motivations behind the action (you know the who, what and where but not the why). Talk to your staff, what have they observed with customers in their stores. What discussions at the register stand out? Check emails from customers, call centre logs, social media accounts – what does the vocabulary, tone of voice, expressions and topic of conversation tell you about why customers buy from you.
If you don’t have a loyalty program, purchase card or some other way to track customers, you can still develop personas but your staff will be vitally important in the process. Ask each store or business area to draw their typical customer – there may be two or three types of customer. Important attributes are age, gender, dress, accessories (prada handbag or guess tote), length of time in the store, attitude (rushed and abrupt or chatty and engaging), in fact anything that they observe should be recorded.
With completed personas you can extrapolate on how they might react to marketing campaigns or to business changes, which can give you a helping hand with acquisition campaigns and provide guidance for reactive areas in your business – like the contact centre.
So far the data analysis has been useful internally – to help you make more insightful decisions generally but not relating specifically to a single customer. The next parts in the series move into data and individual customers and how you need to avoid crossing the ‘creepy’ line.
