Build the Business Case for Analytics Products: 7 Best Practices

by   |   June 13, 2017 5:30 am   |   0 Comments

Southard Jones, Vice President, Product Strategy at Birst

Southard Jones, Vice President, Product Strategy at Birst

Building analytics into existing products is a winning combination. Users get the data insights they need within the applications they already use.

However, it’s not just a switch you flip on. The process of building a successful analytics product that benefits your business and your customer is a journey, and it starts with building a solid business case.

These seven best practices for building the business case for analytics products will put you on the right path toward creating something that people will be attracted to and use.

1) Use both qualitative and quantitative factors in assessing the market opportunity.

Use both statistical methods, such as surveys for gathering market data, and qualitative techniques such as analysis and customer interviews.

To get a complete picture of the market and assess the business opportunity for analytics, you can harness the following information:

Customer interviews: Know what problems customers are trying to solve and what data they need to do so.

Sales feedback: Gather anecdotal feedback from the sales department with details into where buyers are looking for accurate results.

Surveys and market statistics: Market research data and survey responses can be powerful and demonstrate the need for analytics products.

2) Look for high-cost data crunching and reporting activities to prove the case for an analytics product.

More often than not, there is a bottleneck in getting insights to business users. An analytics product can automate these manual steps, while also offering self-service to business users. Quantifying these manual efforts can help prove a business case for creating a smooth, out-of-the-box experience for end users.

3) Take a conservative approach to revenue and financial projections.

Project conservative up-sell rates for new analytics products for the first year. For the following years, you can present more aggressive numbers, given that lessons learned throughout the first year will help you build a much stronger analytics product in the following years.

4) When dealing with an economic buyer, focus on high value, low-risk scenarios.

The opportunity for analytics is directly dependent on the value of decision making. This is true even at places where budgets are tight. The frugal buyer still needs data to run a successful business. To contain your efforts and resources, you should always think about the biggest areas of risk where having the right data point could make a huge impact.

 5) Use a Proof of Concept (POC) demo to lobby for executive sponsorship.

A lack of executive stakeholder engagement and early participation in the project lifecycle will result in downstream refactoring and delays. To garner executive support from the beginning, start with a PoC.

Product teams are increasingly building PoC demos as part of their business cases and presenting them, along with financial projections, to executive stakeholders. This speeds the process and reduces the risk factors.

6) Make sales your ally.

You should always make sure that you have alignment before you present your business case to your CEO, GM, or other stakeholders. You and your head of engineering should be on the same page. However, engineering might not always be your strongest ally.

Make sure sales is involved from the beginning. Get your sales leader excited about the potential for this opportunity. After all, they are the ones introducing this new product to the market. Companies are increasingly putting their beta products in sales’ hands. This not only helps sales show an early preview to customers, but it also makes your sales force part of the initial offering.

7) Know your buy-vs.-build rationale and document it.

Creating an analytics product is an endeavor that will stay with you for years. Make sure you know what you say yes to, what your limits are and where you decide to revisit your approach. And if it is something that you want to build, can you keep up the pace? Do you have the budget, resources and discipline to constantly innovate and to differentiate?

In closing, while there is much value in creating an analytics product, the first step is to set a strong foundation for selling it to your team and your customers. Taking these initial steps ensures that you are ready to define a go-to-market strategy, design the product and successfully launch it.

 

Southard Jones is Birst’s VP, Product Strategy.  He is responsible for the company’s go-to-market strategy, product and competitive positioning, and strategic projects.  Southard was previously the Vice President of Products at SCIenergy, a leading provider of Energy Management Analytics to commercial buildings, where he transformed product and go-to-market strategy, leading the company to a five-fold growth in quarterly ACV bookings.  Prior to SCIenergy, as Vice President of Products, he led Right90, a pioneer in SaaS sales forecasting, from start-up to acquisition. His software career started at Siebel, where he ran the Performance Management and Workforce Analytics product lines in Siebel’s fastest growing business unit, Siebel Analytics. Southard holds a BS in Mechanical Engineering from Cornell University and a MBA and MEM from Northwestern’s Kellogg School of Management and McCormick School of Engineering.

 

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