The concept of using business data to improve organization’s decision-making is not new. It all started with improvements in digital data storage and computing systems in 1970s and within a decade, software systems such as Decision Support Systems (DSS) and Executive Information Systems (EIS) came in vogue. But still, most of the work done during this initial phase centered around intelligent reporting and not actionable business insights. That began to change with the advent of ‘Analytics’ into decision-making, with a few firms such as Amazon, Capital One etc. pioneering the way and devising their competitive strategies based on data-driven analytics. When done right, analytics brought irrefutable benefits and helped businesses create sustainable competitive advantage. However, to a large extent, analytics was (and still is) delivered via custom project model and that presents some serious strategic limitations.
First off, there are clear costs and turn-around time implications, as the value chain from data to decisions is complex and involves many varied tools and stakeholders – technology consultants and statisticians at one end and business decision-makers at the other. Secondly, these stakeholders don’t always understand each others’ worlds perfectly and that leaves wide-open grey areas. Analytics team complain of lack of business support and ambiguous requirements, whereas business decision-makers often get frustrated with slow turnaround or even compare analytical methodologies to black-box whose results they are wary of using. Today, decision-makers want Analytics to provide not only insights but actionable business recommendations and they want them at their own convenience and in a form that is easy to consume (criterion which the de factor standard, PowerPoint reports, don’t always score high on). Recent explosion in the volume and type of data enabled by technologies such as mobile computing, social media, sensor-based technologies etc. has only aggravated these aforementioned challenges.
To summarize, a fundamental shift is clearly underway in how business users like to consume analytics: on-demand and in a more personalized way. On the supply side, technology and analytics vendors are embracing these challenges well. They are leveraging the new frontiers in technology, including Cloud, Software-as-a-Service (SaaS) model and Big Data technologies (e.g. Hadoop) to offer novel solutions based on rich visualizations, real-time tracking and, powerful predictive analytics to count amongst many.
One such path-breaking approach is ‘Productizing’ analytics services or in other words, creating products based on standardized and repeatable analytics services. Many businesses have shown preference to switch from the project-based consulting to these product offerings as they epitomize the ‘Decisions first’ approach and offer several advantages including accelerated delivery of solutions at a significantly cheaper cost. More importantly, Products can lead to true democratization of the analytics as business users don’t have to depend on their analytics partners/vendors to churn the analyses and can access the ‘analytics’ whenever they want it and how they want it. Consider this – A CPG brand manager logs into marketing analytics product through her smartphone to check latest month’s revenues and ad effectiveness numbers. In addition to showing historical data, the analytics platform also simulates revenues and media ROI by how next month’s budget is allocated across media and finally, shows top 2-3 recommendations on how the budget should be spend to meet the targets. With-in an hour, she has analyzed the numbers and figured out an optimal ad spend allocation for the next month. Sounds too good to be true? Actually it’s not.
Few such technology driven products which use analytics to turbocharge them are already in the marketplace and many more are proliferating. Not surprisingly, this space is being disrupted by the nimble-footed startups who have developed products to solve for very niche challenges and answer high-value questions that businesses are facing. Pick retail sector for example. ‘Manthan Systems’, an India based start-up has developed a range of products for retail industry to manage their demand and supply better. One of them – ‘Demand Signal Management’ – harmonizes retail data with syndicated shopper data to give real-time view into consumer shopping behavior. Across the globe, innovative start-ups such as ‘Sysomos’ and ‘Crimson Hexagon’ have capitalized on proliferation of social media to launch analytics products around social data. One of India’s other top analytics provider ‘Absolutdata Inc’ has pioneered the ‘Decision Engineering’ approach and recently launched a product to help SaaS businesses increase their trial user engagement and conversion.
At the same time, traditional analytics providers are also waking up to productization as it translates to a host of benefits for them – not only it enhances the quality and scalability of revenues and increases profitability but also makes the business more predictable and sticky with clients. For instance, Accenture has launched ‘Agile Marketing Analytics Platform’ (AMAP), a SaaS based product that enables clients increase effectiveness of their marketing dollars.
There are lots of other examples spread across the industry spectrum from the financial or retail sectors (which traditionally have been early adopters of analytics) to hospitality or even new-age digital businesses. We could go on but think the point has been made. Clearly, products or productized solutions are the way forward as they rapidly democratize high-level analytics capabilities where clients can plug into a hosted cloud to access fairly sophisticated analytics. Products are also one of key and potentially the biggest growth driver for the traditional and new analytics providers.
Authored by Richa Kapoor, Manager – Marketing at Absolutdata