As I had promised in my previous post, I start off today’s post by explaining how an Enterprise can operationalize analytics. The biggest challenge before any Enterprise, be it a B2B or B2C company, is – getting to know the customer. Build a customer interactions database. This is the first of the six important steps in operationalizing analytics for any company, and often one where many falter.
But before you go down that road, you need to analyze your company’s products or services, to get answers to:
- How will I sell my products/services?
- Who’s interested in buying my products?
- (More importantly) Where do I sell?
- Where are existing customers buying from?
If you think that drawing a customer profile is a simple matter of clubbing clients under generic labels like, ‘Men, aged 30-40 years’, or ‘Firm with less than US $50 million annual revenue’, well, it’s not so. Such simplistic ‘profiling’ of your customers is not enough. Why? Not all men in a particular age bracket have the same spending power, for example. Thus, your company cannot afford to draw up “half-baked” inferences about your customers.
There’s a simple mantra behind building a customer profile base: draw up as intricate a profile of the customer as possible.
A lot of effort has to be put in to understand customers:
- Ascertain all your customer touch-points and assimilate the data
- Using this data, calculate the value of each customer, i.e. what does he cost your company to acquire and retain, and what kind of revenue does he/she contribute
- Then, profile your customer base to establish the characteristics of your ‘most valuable’ customers
We, at Express Analytics, for example, deploy a variety of analytical techniques to decipher customer data collected over social media, emails, by geo-location, and across channels and devices, to help Enterprises find out who their customers are, and the value of each.
Social: Social media, we feel, has become one of the most important data sources. Start off by appending the Twitter handle, Facebook account name, WhatsApp account, to every customer’s data base account. Then, build in an automated signal that alerts the company when a customer uses certain ‘keywords’. Also, every time a consumer ‘likes’ something on Facebook, it must trigger off an alert. Using a consumer’s social journey, across multiple social networks, over a period of time, a clear image of his/her likes, dislikes, even spending power, can be ascertained.
Emails: Email analytics is another valuable source to be used to build a credible customer data base. First, our dashboard tracks every aspect of an email – the number of bounces, the number of emails opened, links clicked on, and ‘unsubscribe’ behavior. It is also necessary to integrate data from other email service providers used for email marketing strategy for your customers like MailChimp and SilverPop. Find out which types of emails resonate with a particular recipient by interpreting the email analytics.
Channels: Find out the channels from where customers are buying your product or service. Channels mean whether customers are using browser, Twitter or WhatsApp or email or SMS. Tie in all the data in with the customers’ profiles.
Devices: Identify their device preferences whether they are a desktop or laptop user mostly on a desk or are they mobile user. Desktop, mobile, Android, Windows, the apps they use…and so on.
Operating Systems: The various operating systems used by the customer in the process. Windows, MAC OS, Unix, Linux, iOS, Android, etc. Each can allow you different levels of personalization and customization to improve the intimacy of the customer with the brand.
Third Party Integration: Integrate the profile of customers obtained from third party consumer data collection companies such as Datalogix, and agencies such as Nielsen, Experian, BlueKai, Master Card, and with e-commerce engines such as Magento.
Build a Customer Context: Build up a detailed database on your client and capture everything you know about them, including contacts and conversations. Collect a lead’s life events such as birthdays, marriage anniversaries, divorce, vacation plans, movie outings, weekend plans, wish-lists, martial status, kids, zip codes, income bands, degrees, shopping habits and preferred stores. Customers may also be grouped by similar psychographics variables such as values, beliefs, buying patterns, and lifestyle choices.
Demographics: This is the simplest step in the building of a customer base profile. Customers may be grouped by similar variables, such as age, gender, occupation, education, or income levels, geographic location, industry, number of employees, number of years in business, products or services offered, or other defined criteria.
Thus, having successfully built your customer profile data base, the next step in operationalizing analytics is – Building a Prospects’ List, which is the subject of my next post.