When you invest in a technology, you want to know what the return on investment will be. Big data is no different, except for the fact that returns here are somewhat uncertain. How do you determine big data ROI? There are no historical budgets for big data so that goes out of the window. Traditional ROI models are also no good as they factor in elements that are outdated. So your starting point is to develop an understanding of how to define big data ROI. On its own, data has no “dollar” value you can attach to it; it’s the actionable insight derived from data that creates value. And therein lies the problem. Any potential value from using big data is uncertain because the potential insights and the resulting success are uncertain, meaning conventional ROI measuring doesn’t apply.
In order to develop big data ROI, here’s what you need to understand:
Big data is not a product or a service.
While big data itself has no innate value, the returns are gained from insights it provides, or more precisely - actions that are a direct result of those insights. That’s the context in which ROI should be viewed: as a return on the final shape and form of the big data insight. In other words, there’s no universally quantifiable way to measure ROI; it's a per-basis approach. Set an expectation/objective -> analyze data -> convert into actionable insights.
To do so, there are some steps to follow.
Understand your need and use for big data
There’s a perfectly good reason why it’s called BIG data: there are tons of it available and not everything is relevant to your sales team. You need to set an objective for what you want to achieve. For instance, you may want a better understanding of your competitive landscape – to see your place in the big picture and how to stand out. Maybe you want to step up your prospect and lead generation by gathering necessary information and crafting a profile of your ideal customers. You can also use big data to get a thorough grasp of your current customers so you can provide them a better experience and form a long-term relationship. Picking out the right objective (or more of them) can help determine big data ROI.
Image credit: SRH Hochschule Heidelberg
Pick the right tool for the job
To meet your objective(s), there needs to be a means to an end so you need to choose the correct tool that will be the key driver of your success. Different businesses use different solutions so you need to carefully think through your decision as some tools require additional tools to fully function (i.e. you need a cloud storage solution if you’re big on storing loads of data).
In that regard, your best bet would be a digital platform such as SimilarTech’s Sales Insights Platform that is specifically designed to leverage data and provide critical insights into technologies from across millions of sites on the world wide web. With over 300 million websites fetched and analyzed every single day, our data is always up-to-date and thanks to our partnership with big data titan SimilarWeb, our web traffic data and websites' categorization sets the industry standard. 99.99% of active websites over the Internet are reached and indexed in our database so you can efficiently gain all the insights you need, be it market research, prospect and lead generation, customer retention, or else.
A solution like SimilarTech can be the much-needed long arm of big data for both your sales and marketing departments. You can easily find the right prospect, market, or vertical to set your foot in and obtain vital information to make bigger deals, resulting in a positive ROI. It’s an effective and straightforward (as much as possible) way to shorten the sales cycle and help your salespeople easily obtain actionable data to drive sales and grow the company.
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Many ways to measure big data ROI
To measure ROI, you first need to assess the cost of big data engagement against the accuracy and relevancy of the insight, then gauge how that insight transforms decision-making into money. That way, you get a quantifiable support for your business’ decision making. In terms of uncovering greenfield opportunities, big data can be used to test a new product/service, as well as a new market by analyzing both product/service and customer data and crafting an overall responsiveness and acceptance to it. If the data shows a solution that is likely to be rejected, then you measure ROI in money saved. If you want to improve your sales teams performance, big data can provide more accurate customer targeting, where you can learn a lot about their buying habits and history, pain points, best customers, and so on. In that case, ROI is calculated by an increase in sales, better customer loyalty, and appropriate performance metrics.
As one of the best practices and advice I can give you, start with a small project that will achieve your objectives and then scale up. The investments needed for such a project are generally easily attainable budget-wise, and the combination of costs and returns can give you a much more precise and valuable perception into the return on investment as opposed to benchmarks and/or historical data.
For the most part, your ROI is dependant upon the objectives set and tool(s) selected for the job. There are tons of additional variables that affect the outcome of big data ROI, such as the size of the company, the cost of big data technology, etc. If chosen wisely, big data will provide a positive ROI and yield valuable insights your sales reps need to succeed. By discovering problems your potential customers have and thus gaining a competitive advantage, big data returns will depend a great deal on the types of questions asked by your sales reps, as well as how much the data itself is able to tell you. This means that your return on investment will be measured (and I can’t stress this enough) by actionable insights that will boost your bottom line. If used properly, big data can show you the exact value it brings and how long it will take before a return is made. With available tools such as SimilarTech, hitting your goals shouldn’t be hard to achieve.