Use Big Data Analytics to Evaluate Potential Business Partners

by   |   July 21, 2014 5:30 am   |   0 Comments

 Use Big Data Analytics to Evaluate Potential Business Partners

Jonathan A. Flatow, CEO, Avention

Most business transactions, regardless of type, begin with an element of research. From customer acquisitions, mergers, partnerships, or a simple sales transaction, what starts as an idea turns into criteria from which the research begins, typically feeding into your main objective. But as senior leadership calculates the company’s mid-year status against annual projections, it’s critical to proactively streamline the research process to drive revenue now. Every minute spent on a lead that will never turn into a sale is a minute wasted instead of pursuing truly viable prospects. How can this be avoided?

For sales or other business transactions, the financial health of the partnering company must be accounted for during the critical research phase. Luckily, business information and big data analytics have progressed to a point that they offer a wealth of insight on private companies that is not typically available anywhere – not even on a credit report.

The process by which relevant opportunities are identified is critical because most leads never become customers or business partners, even when it appears all systems are go. A potential partner may have expressed initial interest in doing business with your organization, but recognizing whether that company is actually in a position to do business depends on your ability to mine the right signals from an accurate data set. Focusing on financial insight during the research process changes the standard funnel that sales and research teams typically follow so that it more closely resembles a robust pipeline – in other words, a greater pool of viable opportunities is available from the start.

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On average, contacts in your CRM age at 2 percent per month due to new job titles, company name changes, office relocations, etc. After two years at that rate, four out of every nine records are no longer accurate. Old, inaccurate data can be harmful when engaging with other companies. In addition to potentially damaging a relationship, wasting time, and, of course, risking embarrassment, the result is often a dead-end opportunity.

Unfortunately, your average CRM database is not always equipped with the ability to keep up with data quality to the extent that it uncovers the most relevant, timely opportunities. This is where the pipeline-versus-funnel comparison comes into focus: The most efficient businesses have technology and research processes in place that use real-time data analytics not only to identify the right opportunities, but also to disqualify the wrong ones quickly and easily. Rather than purchasing a broad list of stagnant company profiles, business information technology is guided by experts in library and information science whose sole job function is to collect, categorize, exclude, and enhance the data being searched. These specialists compile comprehensive insight far more valuable than the delayed information on a credit report or the limited facts that are publicly available.

After you make a commitment to data quality, the next step is to establish a research process that applies concept-based parameters to help big data analysis identify opportunities that fit your company’s priorities. For example, an organization may want to work with companies that are entering a new market or were involved in recent legislation. Identifying these priorities will allow researchers to focus their time and energy on the most relevant prospects.

When research isn’t relying on static, out of date, irrelevant data, you can identify the signals that will tell you whether or not a prospect is financially viable and capable of engaging in a business transaction. One major signal to consider is real-time spending and risk data, which can identify patterns in spending and payment history. This offers granular detail into a company’s financial status and can raise a red flag, if necessary, for decision-making conditions in a manner not attainable through financial reports or credit bureau information. Even if you obtain a credit report on that company, you might receive outdated information instead of an up-to-date reflection of what the company is going through right now.

When leveraging business information and big data analytics, here are a few more signals to look for when evaluating the financial situation of a potential partner:

  • A customer of your company has put a hiring freeze in place. The bigger picture here is that the company is pulling back on further investments. To make the most efficient use of their time, salespeople should shift from organic growth to underscoring the value of your product.

 

  • A company in a market you are interested in entering is consulting with industry analysts on new solutions. Your marketing team can develop collateral with tips on what to look for in that type of solution and how your solution can be applied to their type of company.

 

  • A company you’ve been liaising with for several months just made a large investment in new office space that they paid for in stock. If your business information technology picks up on a signal you set for “companies expanding office space,” this purchase would be flagged before competitors even know they are expanding.

 

Companies should not waste precious time on unqualified leads when it is better spent growing their business. Endless hours of moving a lead through the funnel only to find out that financial restraints will prevent the transaction from being completed can be avoided. After relevant companies are identified, incorporating spending and risk data into your research should be the top priority to ensure that you and your team are focused only on the most qualified and viable leads.

Jonathan A. Flatow has more than 23 years of executive management experience and is the chief executive officer at Avention, which specializes in business information and Big Data analytics reporting.



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