Marketers live in a divided world, separated into the data ‘haves’ and data ‘have-nots’
The Data ‘Haves’
There’s a special class of marketers that we all look up to, let’s call them mega-marketers. They generate $10 billion to $100 billion in annual revenue, and every year they spend hundreds of millions, if not billions, on marketing programs, including lots of paid media. They have CRM systems where they bring together data collected from customer interactions, partners, and external sources. They have a team of Ph.D. statisticians and data scientists on staff. The data team mines the CRM data to generate insights and report on performance, and uses it to select subsets of people, both customers and prospects, to target in marketing campaigns. After each campaign, promotional and transaction data are merged and mined to measure results and inform future efforts.
The learnings from these activities get built into processes that allow mega-marketers an enviable degree of predictability in their top line. Armed with these data management capabilities, mega-marketers can make generating revenue look a lot like a manufacturing process, at least in the short run. Put the right data in, define the objective, apply the recipe, and a relatively predictable volume of new customers or revenue appears more or less on schedule.
For instance, big telecom marketers tune the size and timing of promotions to reflect the value and likely response of each recipient, across every marketing channel. Retailers use purchase data from loyal customers to design campaigns that promote a specific supplier’s products, paid for by the supplier, including reporting back to the supplier on the ROI of the campaign. Big auto companies concentrate on relatively small segments of people predicted to be highly likely to buy a car this year, based not on clicks or any overt consumer action, but on a battery of characteristics that are collectively highly predictive, including past car buying behavior, current life stage and car ownership, location, and financial means. From these elements, not only purchase likelihood but preferences by brand, vehicle, and financing type are effectively inferred. And, credit card companies slice and dice the population every month to match their own evolving needs for growth balanced against tolerance for risk. Armed with sophisticated data and analytics, the financial marketer selects which individuals to invite to accept each credit product or feature, with what set of terms, backed by a highly accurate projection of acceptance rates that in turn enables forecasting of financial performance months and quarters ahead.
All this data management and infrastructure costs eight figures annually, but that’s a small piece of their nine and ten figure marketing budgets.
Make no mistake, marketing is an imperfect science, even for these well-heeled and disciplined mega-marketers. Competition, economic factors, changes in consumer taste, and random events interfere with predictability. Creative concepts wear out, products lose their edge, etc. But, in the short run, working carefully and using the learnings from the recent past, these marketers pretty much control their destiny, trading off near and long-term benefits to meet current requirements while investing in customer relationships that will pay dividends in future quarters.
The Data ‘Have-Nots’
By comparison, the bulk of marketers, or mainstream marketers, live in a constant state of data poverty.
With marketing budgets far below that of the mega-marketers and growth goals aplenty, mainstream marketers generally survive on only the systems they need, and many times have had to beg for, nothing extra. Mainstream marketers are forced to make hard choices when it comes to the channels they engage in and the resources they have to activate those channels. In basic terms, a mainstream marketer might have a website, with some SEO and paid search, a presence on a few social media sites, a one-size fits all email program to push promotions out, and an agency to do a certain amount of outbound programs, events and media. Customers more or less present themselves on their own initiative, either via direct or retail channels, or both. If a mainstream marketer is able to scrounge up the dollars, the time, and the know how needed to understand customers directly, maybe once a year they’ll go to a syndicated research provider like MRI or IRI to commission a study that profiles customers based on a small sample drawn from the provider’s panel. That’s about it.
Quite a contrast to the mega marketers. The mega’s harvest every gram of customer data, refining it into insight to help measure effectiveness and ‘signals’ to help define the next wave of targeting segments. For the mainstream marketer, customer data is mostly untapped. It remains buried in fulfillment records, in the systems of channel partners, in their billing system, in web and social management platforms, in the email platform, and embedded in spreadsheets and platforms at the agency. It is not extracted or collected, let alone enhanced with external data and analyzed.
Hope for the Mainstream Marketer
While the data gap between mega and mainstream marketers is large, there is some good news for mainstream marketers: It turns out, there’s never been a better time to start leveraging the value inherent in a company’s customer data. For one thing, marketers of all kinds are having more direct interactions with and direct access to their customers, so more customer data is being created that belongs to mainstream marketers. Plus, the cost of storing and analyzing data has plummeted and will continue to do so. And, the quantity and quality of insightful data becoming available to help understand customers is accelerating. Finally, the willingness of companies of all kinds to partner and share data to drive shared insight is also growing, making it possible to break down barriers that have traditionally kept data in separate, company-specific silo’s.
Where and how to begin?
Here are a few guidelines to consider:
Don’t build your own asset, rent one that’s already built. Most of the work and investment required to market based on customer data is the same for every company that tackles it. So, it makes sense to find a service that has already built a database that contains all of the data you need to generate insight, that is already set up to produce segments and push them to whatever channel you need the them to be, and can ingest data from your or your partners to create end products that meet your unique needs. Find someone who will sell you the quantity of expert analytic service you need, without having to build and manage your own staff.
Start by choosing a way of marketing that already works well for you, and then use data to make it work better.
The best way to evaluate and capture the power inherent in your customer data is to use it to improve the results from something you are already doing. That way, you have an established yardstick for comparison. If you are simultaneously working to understand a new channel, experimenting with new creative, and employing data for the first time, you won’t know whether poor results are because data doesn’t work or you just haven’t mastered the new channel, or how much of any success you achieve to attribute to the data itself.
Don’t be drawn into pursuing world peace; focus on quick wins. Mega-marketers who are already experts at using data tend to dominate the conversation about data-driven marketing techniques. So, in the marketing press, one hears and reads most about the latest challenges facing the mega-marketer. For instance, many of the largest marketers these days are trying to determine how to disentangle the effects of different forms of marketing on the same customer, through a methodology called multi-channel attribution. If you are already using a dozen paid channels to reach your customers, wouldn’t it be great to know how much credit to award each one for every sale, so you could balance your investments? This is an example of a very tough problem, and it certainly would be great to know the answer. But, it’s a problem that people should only tackle once they’ve already gotten pretty good at using data to manage and measure the individual marketing channels that are most valuable to them.
With a cost-oriented, practical approach, any marketer can break out of the cycle of data poverty, and start turning the unique knowledge inherent in customer identity and behavior to drive successful customer acquisition, upsell and retention. It’s the dawning of data democracy.