“Everything comes back to Big Data.”
This was a network security consultant’s conclusion. He was telling me about an international retailer’s data breach. The retailer had warnings that a breach was imminent. However, those warnings were lost among the millions of ultimately benign but undifferentiated notices of possible attack.
What was missing, my friend concluded, was some type of signal that isolated the true threat.
I was reminded of the customer intelligence equivalent: the doorstop binder, or reams of analysis, or zipped data file, all pushed blandly across the table. The work contains anemic results, often with equally weak observations, but no intelligence, no relevance, no significance, no value.
These results are always pushed across as completed work and the presenter smiles triumphantly. However, the work has barely begun because no one has sifted through the great pile of data to extract the signal of greater opportunity.
Deriving customer intelligence from data represents such tremendous potential. That potential is greater understanding of customer needs, greater insights to what drives response and interest, the potential to reduce costs through greater efficiency, and ultimately there is potential to speed and increase revenue.
There is nothing but potential.
And with data sourced from input, from behavior, from things, that potential only grows.
What is also growing is the gap between all that potential and what companies actually realize.
The impact of neglecting to create intelligence is borne out by a distressing statistic: prospects are 75-90% through the decision cycle before contacting sales.
I spoke with a CMO recently who claimed that he and his team were experts at the new way technology is marketed. I was taken aback by the assertion. It’s not that I doubted him. But what matters is how prospects are buying. We necessarily must market to their needs and behavior.
If prospects’ behavior has changed, or if there are additional resources in the decision process, or if some new need has emerged, then of course, we should adapt and evolve. But the core marketing objectives remain: create awareness, establish value and build preference, drive an efficient decision and purchase. The only reason to adapt is when the target customer gives us reason to change.
What also surprised me about the assertion was that there are so many common problems with tech marketing, I wondered if the CMO had solved them. I wondered if that is the “new way technology is marketed.”
Let’s work our way through the list and consider all that could be improved if we addressed these issues:
Data is the raw material for deriving business and marketing intelligence.
So much has to go right to form that intelligence: ensuring that data is accurate, clean, complete, available, relevant, applicable, and actionable.
And those are just table stakes.
Actually deriving and optimizing rich intelligence from data and its analytic results is the tricky part. Absent critical thinking and deep understanding of customer behavior, and the business itself, data-driven intelligence will only be squandered, cheapened, or damaging if the only approach is identifying the next-most-obvious tactic.
This root-cause analysis for two different clients serves as a powerful example. Reporting revealed the same result: high email opt-out rates.
While the results were the same, the underlying drivers and the real opportunity were completely different.
Let me explain….
For some of us, extracting intelligence from “big data” has been a practice for decades or more. We simply didn’t have a convenient buzzy term for it.
What hasn’t changed over that time is the necessity to extract meaningful, actionable intelligence and apply it to grow a business.
Factoring each business’s unique environment, opportunities, competition, resources, limits, etc. are all necessary in order to make the most of intelligence derived from data. One size does not fit every situation.
Quite necessarily, we cannot be seduced by the easy answer that counting clicks or tracking response rates is a measure of the ultimate results we intended to drive.
A CMO recently ask me about my cost to acquire (CTA). I was taken aback as my business is not comparable to his in any way. He had no context with which to judge an answer (ultimately not offered). Whatever my answer (“6!”), he had no way of assessing it to conclude that I am either a marvel or a hack.
I reached a conclusion instead: He simply wasn’t ready to manage his business with data of any size.
Same Data, Wildly Different Results
I’ll draw an example from over a decade ago. I worked with a telecommunications company that captured data for every call—its source, type, time, duration, destination, switching routes, costs, etc. We had data for every call to and from every business across the country. It should come as no surprise that this generated big data.
From this data, the client conducted analysis and created a customer segmentation strategy. It looked like this:
I had a great conversation with Barry Moltz for Business Insanity Talk Radio, a show on Chicago radio station WIND.
Check out our interview to hear about the advantage that small businesses can create
when they effectively harness the power of their data.
We are always interested in speaking to you about your specific data-related opportunities. Contact us! We’re ready to help!
© 2016 Hampstead Solutions LLC
All Rights Reserved
I so enjoyed my interview with Carol Roth for Entrepreneur.com.
Small businesses are in an enviable position to create competitive advantage from the data they generate. Most importantly, small businesses can respond nimbly to capture the newly recognized opportunities that data can reveal.
Read on! I offer eight initial steps to set small businesses on the path to making the most of their data.
While these steps are a great start, we are always interested in learning more about your company’s particular objectives.
Please do not hesitate to contact us if you have a data-related opportunity to discuss!
© 2016 Hampstead Solutions LLC
All Rights Reserved
Technology supports the collection of data from a myriad of sources: websites, real life, social, on and on. That data has so much potential for improving our understanding of customers’ needs and their decision processes. Because of the opportunity to improve targeting, speed the purchase cycle, and build customer relationships, all that collected data promises powerful competitive advantage.
To achieve the promise, however, we must engage with the data. We must extract meaning. And yes, we must market differently.
Let’s use a familiar example: new movers. Marketers have a long interest in identifying new movers and for good reason—there is tremendous revenue potential across a spectrum of highly predictable categories all resulting from a move.
Let’s talk specifics:
And then the race is on! Marketers scramble to deliver their messages, hoping to capture their share of $136 billion.
There is a critical behavioral fact that is routinely overlooked and misapplied:
Most purchase decisions are made prior to the move.
This insight should direct us from an analytic and a marketing perspective. By the time we start running, the race is already lost.
Of course, we are all talking about big data. And we should be: It promises precision, real-time targeting, improved engagement and acquisition, competitive advantage—and faster growth and greater revenue.
To realize the full promise, however, we must admit that big data is simply the raw material input to the true sworn value: intelligence.
Intelligence derived from big data supports better decisions, informs strategies and corresponding tactics, and powers iterative improvement to more efficiently reach our goals.
Transforming those raw materials into intelligence requires specific expertise.
Big data does not conveniently spew intelligence. A big dataset may be varied and vast, so it requires experts to select and extract meaningful intelligence—and apply those insights to strategies, tactics, and decisions.
Commanders of big datasets have four particular, well-honed areas of expertise: foresight, selectivity, discrimination, and interpretation.
1. Foresight: Identifying the intelligence that will support strategic definition.
Success begins at the beginning: with strategic objectives. Declaration of intention(s) is critical and it must be clear. Setting the point on the horizon will ensure that all tactics, including analysis, support laser focus on the goal. It is too easy to become sidetracked—or overwhelmed—otherwise.
I have worked with clients who are awash in data. Every analytic-driven professional dreams of these rich datasets. These same clients, however, can be crushed by the metrics they can generate.