Sales and Marketing in the Age of Data: Challenges and Opportunities
"If land was the primary raw material of the agricultural age, and iron that of the industrial age, then data is the primary raw material of the information age."
The Age of data
The volume, variety and velocity (the famous three Vs of big data) of the data currently being captured is unprecedented. As recently as 2000, digitally stored data was a mere 25% of all data generated but within a decade, by 2007, that number jumped to a whopping 94% (and has not fallen since). To get a sense of the scale involved, consider that in the time it takes you to read this sentence (roughly 6 seconds for the average reader), Google would have received half a million queries from around the world.
The opportunities that the Data age is throwing open to agile, alert and unafraid-to-try-and-fail businesses are numerous, varied and cropping up in unexpected places. Let me illustrate the opportunities and challenges involved using an example.
A Motivating example
In Kenya, Su Kahumbu developed a text-and-based mobile app called "iCow" to help dairy farmers raise yield, and built a successful subscription based business around it. The idea appears simple enough. The app collects information from farmers on milk and breeding records, sends text messages on when their cows have their gestation periods and hence shouldn't be milked, feed timings and amounts, and texts about other dairy best practices. Over time, farmers have learned to trust and approach iCow for other needs - "cows unwell - recommend a vet", "what are prices like in which markets today" etc.
With time, iCow is now becoming also a platform for information and purchase/transactions of livestock. Today, some 11,000 farmers - who are small businesses in their own right - avail of the subscription service. As iCow's database increases in size and quality, it is able to offer ever better prediction, services and value to its clients, which they are willing to pay for. It is calculated that an iCow subscription pays back for itself in under 7 months. The average iCow subscriber has 3 cows but within the first year, dairy yield has jumped to the equivalent of having 4 cows. In fact, so compelling is the value proposition that a $1 investment in iCow yields a $77 return over 3 years for farmers (so there's much headroom to raise prices, expand services etc.). And remember, all this is in Africa, with nothing more than a text-and-voice based system (and subscription payments are handled via M-pesa directly on the mobile, besides).
One may ask what is the relation to India and Indian IT? Well, we have 100s of 1000s of small businesses in different stages of maturity but one thing is clear - all of them have access to text-and-voice based mobile services. perhaps there is an opportunity,, at least in some sectors whereby a market-making, analytic, data-central platform could be built around a subscription model with as compelling a value proposition as iCow has had? Of course analogies have their limits, but imagination has none. Different sectors will require domain specific solutions and the point is that today the tools are all available for venturing into the vast SME space with the beginnings of a break-even business model that could later yield ever more opportunities.
Returning to more traditional IT domains, the typical Sales and Marketing funnel at most B2B firms today goes something like this: A large number of prospects (marketing qualified leads) go into the top of the funnel and are then progressively screened and filtered by both sales and marketing personnel until a focused set of sales-qualified-leads emerges that sales then pursues and hopes to convert into business - prospects into customers. Thereafter account management principles enter the picture and the relationship is managed to mutual benefit.
The importance of data in this entire process can be seen clearly. Some (though not all) prospects fail to materialize because of faulty, obsolete or otherwise irrelevant data. In other words, data hygiene - clean, updated, relevant pieces of information - plays an increasingly important role in every stage of the Sales and Marketing Process -
trawling for prospects, knowing where in the ocean to cast nets in the first place, assessing prospect needs and readiness to transact ==> knowing what to seek in an RFQ and how to pitch the same, etc. An increasing portion of Marketing-Qualified-leads that fail the screening process could be salvaged if timely, updated, clean and relevant data could be had on tap. The value of this data implies firms' willingness to pay a premium to access it. Indeed, Value is often called the fourth V of Big Data. Big Data that pools in multiple sources of data can be used to cross-check, verify, update, flag and repair erroneous data in firms' databases.
The Data Hierarchy
Traditionally the data hierarchy went from data to Information to Knowledge to Wisdom. A data hierarchy more suited to the sales and marketing process might go by Data --> Analytics --> Insight. The analytics piece would deal with updating and correcting extant data in addition to collecting more relevant data as required. And Insight here would refer to what customers ultimately seek from Data - answers, solutions, estimates, predictions, direction - and all which are Novel, (ii) Credible and (iii) Actionable, thereby conferring competitive advantage.
About the Author
Sudhir Voleti is an Assistant professor with the Marketing Area at the Indian School of Business (ISB). Previously, he worked in the industry in different capacities as a management consultant and a software analyst. Professor Voleti’s research focuses on combining data with econometric and statistical methods to explain phenomena of marketing interest such as evolution in the equity of brands across time, valuation of brands using secondary sales data, the sales impact of geographic and abstract distances between products and markets and the performance, productivity and benchmarking of salesforce organizations. His work has been published in leading academic research journals such as the Journal of the Royal Statistical Society (Series A), Management Science and Quantitative Marketing and Economics.