The potential for mobile operators to generate significant amounts of revenue from Big Data is undeniable, solely based on the volume, richness and breadth of customer data that flows across a mobile network. So clearly the key question in monetizing Big Data is how to turn raw data into products and services which corporate customers and individuals will want to buy.
Monetizing Big Data needs to start with the customer
Over the past two decades Redwing has worked with mobile operators in over 30 countries to help them get from business concepts to revenue-generating service launches.
We’ve learned that many operators have a tendency to first launch a product, and then see which customers buy it – the classic ‘build it and they will come’ mentality.
But a far more effective approach is to use a set of disciplined processes that drive product and service definitions from a fundamental evaluation of customer needs:
On the surface these look like simple questions to answer.
But in reality, these are extremely non-trivial questions, and require significant amounts of new thinking on the part of the Big Data team.
Who are your customers?
Are the customers for Big Data your existing corporate accounts? Or do you see potential within the small and medium business sectors? Or is Big Data something that can be sold to individuals?
Should you sell Big Data to the customers’ marketing departments? Or through their existing ad agencies? Or to their IT groups?
Or is the market defined by product and application: are we selling to buyers of customer profile data? Or buyers of identity services?
The potential answer to all these questions is: ‘yes’. Because the buyers of Big Data services form a complex multi-dimensional market which is defined by:
- The industry vertical you are selling to (fmcg, utilities, retail, etc)
- The role of the person you are selling to (media planner, digital media executive, strategic planner, etc)
- The type of Big Data product you are selling (customer profile data, location data, real-time data, etc)
And understanding the interaction between these customer types is key to identifying real, growable markets for your Big Data products.
What do your customers want?
The bad news
Customers themselves often don’t know what they want from Big Data services. Obtaining Big Data products from mobile operators is new to them too, and they often don’t know what is available or what could be available.
There are three basic approaches to help customers tell you what their Big Data needs are:
- Set up mechanisms to poll your potential customers, such as social media analysis, crowd sourcing tools, market research programs or customer feedback panels.
- Build relationships with exemplar customers in the key verticals you wish to target, and learn from them.
- Look for opportunities to integrate your high-value data into their existing systems and programs – evolution is always easier than revolution for customers.
How do customers want your Big Data products to be packaged?
There are two issues to consider here:
- What types of Big Data products do customers want?
- How do they want those products to be packaged and delivered?
Redwing has identified five product families where mobile operators can provide high-value data to customers:
We examine each of the product families – and the ways that products can be delivered to customers – in more detail in our Productizing Big Data page.
What are the elements of service delivery excellence?
Rolling out Big Data products is not only about delivering high quality data analytics, it requires attention to all aspects of the service delivery.
In helping operators with product and service design, we focus on 5 main areas of service delivery:
Service delivery critically depends on how your Big Data team is structured, and we examine organizational issues in our Organizing for Big Data page.
Providing integrated end-to-end service delivery benefits operators, both in the obvious sense of customer satisfaction, but also in building closer customer relationships which provide valuable insights into new Big Data products and services.