I have been reflecting on my ideas, thoughts, and philosophies and going down exciting rabbit holes to see what ideas/philosophies have remained steady and what ideas have changed. It’s funny; as we do this, we may often be surprised by what beliefs we are comfortable re-examining and what stays stuck with us. I intend to reflect on various parts of my life in a series of posts.
Here, I reflect on an area/subject/philosophy I was deeply involved with for over twenty years: the power of using “customer and other data “in business. I built a marketing career out of this belief and, along with different partners, created a company that continues to do well today, although I am not a part of it anymore.
I am pulling out my old writing, written over 5 to 6 years ago, and looking at what has stayed true and may have changed.
I could replace the word “analytics” with “AI,” and everything else would still hold. Raw data is even more critical for businesses today in the world of large language models. Leaders still view this as a space for specialists, and there isn’t enough talk about creating a “data culture,” which will generate the datavore capability that I discuss in the article below. Despite so much hype about AI, my bank still calls me for selling irrelevant products, and that has not changed.
What has changed for me is that I don’t see “data” as the core change agent for companies; in fact, I see many adverse effects of data, such as the ethics of using personal data to drive business. I see the “leader” as the core change agent—and can she/he leverage data to create a better environment for customers, employees, and other stakeholders? That is more important to me now.
I created a company with my partners whose vision was to help companies leverage their data better. The vision holds for businesses even today.
Many years ago, in the late 1990s, I became more of a data-led marketing specialist. I think the belief was that you must have an “edge” in what you do and a unique positioning. So, I was not a general marketer but a marketer who had created a “position” for himself in data-led marketing. And then, I was an entrepreneur in a space where I had a distinctive point of view. I think this belief continues for me—that as a professional, you are a “brand” with “distinctive positioning,” and you must work to create that.
So here are my thoughts from 5 years ago.
Why may Amazon eat Shoppers Stop, HDFC Bank, and other legacy businesses for lunch?
Just like herbivores and carnivores, some companies have become “datavores.” These companies have built the culture, process, structure, and systems to most effectively absorb, digest, and act on customer and other data.
Datavores use analytics to identify new commercial opportunities, improve efficiency, and generate revenues from innovative data products and services; they do not use analytics only for cost efficiency.
Is analytics yet another fad? Is there much more talk about it than real, solid action? It does seem so when you look around you as a consumer. Marketers still don’t care as much about being relevant to you. You get that umpteenth credit card solicitation from the bank, which has already sold you a card. And nothing about a physical retail shopping experience makes it personal for you! And yet, your online persona seems to be treated differently. When you go to Amazon or other sites, you get a feeling of getting recommended offers. As a consumer, you flit between your online and offline avatars, and this becomes more obvious. What have these “new age” companies done differently than has disrupted the legacy competitors?
One of the key strengths that new-age companies bring is their “data literacy.” While legacy companies are still, at some level, paying lip service to the whole concept of data-based marketing. So, is analytics just a fad, or have we been oversold on it? Technology players have packaged analytics into each of their solutions at different levels of complexity. They have created a lot of marketing noise around it. It almost seems like buying analytics software or some digital platform will suddenly make the organization an analytics champion. Maybe the issue is much more fundamental. Organizations have to rethink how they leverage analytics. How must they change their company structures, incentives, and processes to create meaningful business impact? The volume of data available to companies continues to double yearly, with new data streams from the Internet of Things adding to the party. Most legacy companies still have a long way to go to extract value from analytics. Apart from a few digital natives such as Amazon, Facebook, Google, Netflix, and Uber, most companies have struggled to realize anything more than average returns from their investments in big data, advanced analytics, and machine learning.
But it is still early days. According to McKinsey, “About 90 percent of the digital data ever created in the world has been generated in just the past two years; only 1 percent of that data has been analyzed.”.
Is there a category of organization that can leverage “data” far more effectively? Who values analytics more? Some industries seem to believe in the power of analytics and base decisions on this. A bank decides whether to give a loan or not based on an application scorecard, or a credit card company spots fraudulent activity-based analytics and stops the card usage. Many other industries use analytics for insight generation, but are they as good at linking analytics to action as the banking and financial services industry (BFSI)?
An essential element that accounts for how data-led the organization can become is the “culture.” It’s a way of working in a business where everybody assembles data to make critical decisions. When Warby Parker selected its first office location outside New York, it considered a large set of variables—Gallup’s well-being index, talent pool, cost of living, number and cost of flights to New York, etc.—and ranked and weighted them as part of the final decision. So, what is critical is that a data-driven organization uses the data as vital evidence to help inform and influence strategy.
Data-led organizations also “test” a lot. They allow the test data to talk and make critical decisions by testing. I think the “new age” companies born out of the internet revolution do this well. Could the enterprise become a full-time laboratory? Digital native companies were built for data—and analytics-based disruption from their inception.
While new-age companies are structured to do this well, a few traditional or legacy companies have also created a differentiator using testing methods. Nigel Morris, one of Capital One’s co-founders, says that the company’s multifunctional teams of financial analysts, IT specialists, and marketers conduct more than 65,000 tests annually, experimenting with combinations of market segments and new products.
Google-executive-turned-Yahoo-CEO-thought-leader Marissa Mayer declares that “data is apolitical” and that her old company succeeds because it is so data-driven: “It all comes down to data. Run a 1% test [on 1% of the audience], and whichever design does best against the user-happiness metrics over two weeks is the one we launch. We have a very academic environment where we’re always looking at data. We probably have between 50 and 100 experiments running on live traffic, everything from the default number of results to underlined links to how big an arrow should be. We’re trying all those different things.”
Here is an excerpt from Carl Anderson’s book:
“It was 1998, and Greg Linden, one of Amazon’s early engineers, had an idea. Why not create recommendations at checkout? Supermarkets put candy in the checkout aisle to stimulate impulse buys. That works. Why not peek into the Amazon.com cart and make personalized, relevant recommendations that the customer might appreciate? He hacked up a prototype, got it working, and showed it around. The rest of the story is best told in his own words:
While the reaction was positive, there was some concern. In particular, the marketing senior vice president was dead set against it. His main objection was that it might distract people from checking out—it is indeed much easier and more common to see customers abandon their cart at the register in online retail—and he rallied others to his cause.
At this point, I was forbidden from working on this any further. I was told Amazon was not ready to launch this feature. It should have stopped there.
Instead, I prepared the feature for an online test. I believe in shopping cart recommendations. I wanted to measure the sales impact.”
And what a success this experiment was! 35 percent of what consumers purchase on Amazon comes from product recommendations based on such algorithms. Amazon calls this “item-to-item collaborative filtering,” it uses this algorithm to customize the browsing experience for returning customers.
I guess the “new age” companies are architected to capture data far better than the legacy companies. But a lot of it also comes from mindset. Many new-age companies do not hesitate to ask customers for their data, secure in their knowledge that they will provide value back to customers in this barter. One simple example is Google’s Screenwise Trends panel, which gives a US$5 cash voucher to anyone willing to share their Internet browsing behavior with Google and its partners, with a further US$5 gift every three months after that. Or take Raptr, an app that tracks users’ video gaming habits in exchange for regular rewards, such as in-game content or free games. Online fashion retailer Zafu allows customers to buy high-end jeans by asking a series of simple questions about the customers’ body type, how well their other jeans fit, and their fashion preferences. The data collection and recommendation steps are not an add-on; they are Zafu’s entire business model.
But these are just the starting points. Other businesses will develop more creative incentives, from loyalty points to enhanced services, to encourage consumers to share their data. The trick lies in making that data central to your business model!
Also, new-age companies have younger employees who are fearless in using data to change the minds of more senior folks. The shared economy of data lets owners capitalize on the first-party data they collect. First-party data can be gathered from a marketer’s site traffic, CRM database, or customer purchase history. With their Android and iOS mobile operating systems, respectively, Google and Apple know the location of every customer’s Wi-Fi-enabled phone—far more location data than any other company could access. The Silicon Valley giants aren’t allowing outsiders access to such data by outsiders as yet. The new-age companies, from Uber to Facebook, hold growing petabytes of data about user behavior—a “customer data moat” they are creating.
Data and analytics are changing the basis of competition. And in most businesses, new-age companies are fighting for market share with legacy companies. So, your investment in analytics capability can pay rich dividends. Former industry leaders invested mammoth amounts into factories and equipment, and new emerging companies invested heavily in digital platforms, data, and analytical talent. However, for legacy companies, analytics is seen as too theoretical. More integration with systems has happened to push decisions to the point at which consumers interact with the business. This is far easier for new online companies that have built their systems around this capability. CIOs and technology teams in sizeable existing legacy businesses are taking the time to act on this. New-age companies are doing very well, though. Recommender systems are very well integrated with the consumer buying process. As new-age companies compete more with legacy companies, the need to incorporate analytics into the business fabric will become even more critical. So Meru Cabs should be able to pivot and compete with Ola by embedding fundamental data and digital thinking!
And yet, Gartner says that only 20% of enterprises will use more than 50% of the total data they collect to gain a competitive advantage. So, most companies capture only a fraction of the potential value from data and analytics. Companies are now looking at new data sources that can bring enormous competitive advantages. In vehicle insurance, where new companies have entered the marketplace with telematics data that provides insight into driving behavior. Also, a new segment is created in personal loans based on data trails that didn’t exist earlier. Similarly, in retail businesses, a massive amount of video data exists that can further bring insight that positively impacts operations.
So, companies must learn to creatively marshal their data resources to take advantage of many opportunities! Software architect Grady Booch had this in mind when he uttered that famous phrase: “A fool with a tool is still a fool.”
In most legacy businesses, analytics is often seen as a support function. The vision is limited to providing data-based support to other business folk. This is a mistake, as what is required is a complete disruption. Do you want your analytics team to participate in more profound strategic and longer-term decisions in the company? Do analytics folks with a deep specialist background have the skills to participate in such initiatives? Can they own a P&L and run the complete digital avatar of the legacy business?
So, more debate is required before an organization can clearly articulate its analytics strategy. The triumvirate of analytics, digital, and alternate channels go well together. Analytics can disrupt the business model, and for companies that want to compete in this data-rich world, a clear articulation of analytics strategy is critical. Companies can then debate which analytics team structure is most appropriate. Should the team be centralized or decentralized? Or you could create an internal consulting organization with resources embedded in user departments. Another way of thinking about this is to create a “hub & spoke” capability with a Center of Excellence model underpinning it. But more fundamentally, how can a legacy company compete with new-age companies and use analytics holistically?
My experience of working in the trenches has been that someone has to lift analytics out of the mindset of a function and help seed it as a part of the business strategy itself. To do this, you must articulate the analytics strategy and expand on how it would impact the company’s structure, incentives, channels, and processes. Companies need to realize that this requires a lot of involvement at the CXO level, and just having an analytics department would not enable that. An internal or external evangelist must push the envelope to create and sustain the strategy. New-age companies do it more naturally; legacy companies must make a solid effort!
Then again, companies must realize that analytics doesn’t need you to solve a technical problem but a “business & social” problem. Most business analysts do not spend much time in business roles. They are super-specialized number crunchers without sufficient exposure to business reality. Even if the managers have some exposure to business through experience across a variety of analytics projects, is it enough? Analytics and data are transforming companies around the world. Yet one of the great difficulties with analytics is that it can be challenging to explain and understand; it is widely held that analytics specialists don’t communicate well with decision-makers and vice versa. As a result, adopting analytics is still not accessible for legacy companies. On one end, analysts are busy learning more specialized and deeply technical methods of analyzing data. At the same time, they are finding it difficult to get themselves “heard” within organizations. Influencing ultimate decision-makers is similar to selling products or services to external customers. Analysts need to understand that when they present ideas to decision-makers, it is their responsibility to sell—not the decision maker’s responsibility to buy.
Does this bring the analytics career into some jeopardy? No, but it asks for creating an entirely new role, often called the business translator. For this, embed a few of your solid analysts with good communication skills in the business. Thereby asking them to play a translator role to embed analytics into the company’s fabric. Analysts must “storytell” to bring analytics into the company’s fabric. But analysts are too one-dimensional and do not embrace the intersection of “technology, statistics, and business.” So, analysts struggle to tell stories. Often, I see journalists doing a far better job with infographics in the media. However, information journalists do not want a career in analytics, so there is a gap in “storytelling.”
Finally, the average age in the “new age” companies is far lower. Younger people are adopting analytics far faster. They are exposed to it in their education and consuming it through their “digital avatars.” They see this often as a “no-brainer.” Older executives are harder to convert to this line of thinking.
If legacy companies relook entirely at how they see analytics, they may lose to new-age companies!
So, will Amazon eat the legacy businesses for lunch? Time will tell, but legacy companies need to notice that data is now a core product and that leveraging it can hugely boost company performance. In general, markets now value companies more than the sum of their tangible assets. So, intangible customer data and how companies use it will be critical differentiators in the future.