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Analytics Translator: Benefits for Small Business Too

JBM Marketing Solutions

In a recent McKinsey Consulting article, “Analytics Translator: The new must-have role,” by McKinsey Consultants Nicolaus Henke, Jordan Levine, and Paul McInerney, the authors outline the impact the analytics translator role can have on an organization.  In today’s digital, mobile first culture, I needed to know more. 

While the article primarily addressed a more corporate audience, in a small business the role of analytics translator, albeit a scaled-back version, is critically important. For the purposes of this article, small businesses are defined as those with revenue of at least three million.  Most of my client base falls into this category and I find myself consulting companies in terms of what I would loosely characterize as an analytics translator.  

So Who Are Analytics Translators?

The article provides an exact description of who translators are, but I found it more beneficial to focus on what they do.  According to the article, “translators draw on their domain knowledge to help business leaders identify and prioritize their business problems, based on which will create the highest value when solved. These may be opportunities within a single line of business (e.g., improving product quality in manufacturing) or cross-organizational initiatives (e.g., reducing product delivery time).  Translators then tap into their working knowledge of AI and analytics to convey these business goals to the data professionals who will create the models and solutions. Finally, translators ensure that the solution produces insights that the business can interpret and execute on, and, ultimately, communicates the benefits of these insights to business users to drive adoption.”

In a small business, the analytics translator can have similar roles and skill sets.  One common skill is domain knowledge.  Domain knowledge implies expert level industry and company intelligence.  This skill allows the translator to view the businesses analytics in the proper business context. This means understanding what KPI’s impact the P&L, revenue, customer acquisition and retention at a minimum.  Within my firm is more over a decade each in the healthcare and financial services industries.  A common element in each of these industries is data regulation and integrity.

Marketing for Small Businesses: Example

In one case, a financial services company with about $400 million in assets under management (AUM) had an issue growing.  With an economy growing in the 2-3% range, the company’s growth was less than optimal, hovering in the low single digit range.  The business plan called for a 13% increase in AUM.   That domain knowledge came in the form of understanding the industry’s culture and the issues impacting it.  That expertise allowed me as the translator to bridge the gap between the head of IT, which is very concerned about data integrity, operational teams, and marketing. 

Many financial services companies operate on legacy technology platforms built before the current Fintech explosion.  So sharing data outside of a defined silo can run counter to the existing culture.  From the client’s perspective, they see thousands of new tech solutions being pitched to them each week – not to mention some vendor each week convincing the client to have a one hour demo.  Because most of the vendors were relatively new companies, there was little confidence in the platforms.  There still exists a mindset of “Nobody gets fired for hiring IBM.”  Knowing that data security is important allowed me to better rank stack the problem and create a solution. 

The company’s data analytics person was producing reports in excel.  Because the reports contained large volumes of data, getting line managers to consistently use the spreadsheets to drill down into the key drivers of growth was an obstacle to growth.  It was nearly impossible to determine which product or group of products produced new accounts and at what level of profitability.  My first translator meeting was with operations to link the data to current business plan goals and to determine what data was needed by whom and at what frequency.  Up until this point, department heads did not have the tools or data to know how they were performing against plan. 

As a part of that process, I made recommendations on the appropriate tech stack to optimize the marketing process (CRM, etc.), suggestions for new product bundles, and sales training.  The first milestone was making sure line-staff had the right data and the right time for leads that showed an interest in specific products and were ready to be contacted.  This type of lead segmentation led to a 20% lift in MQL and a nine percent boost to AUM.

What Factors Driving Role at The Small Business Level?

Two dominant factors are driving the need for a translator at the small business level.  The first is that small business tends to operate very lean. The need for a full-time translator is not fully understood either. In a typical company, senior management is analyzing data from a dashboard and using the data to make a day-to-day decision.  This type of analysis is often done without interaction with other departments.  My job is to bring the various internal groups together to create a shared vision of the process and define how each department’s KPI’s roll up to the organization’s revenue goal.

The second factor is technology itself.  Many small business owners are over the age of 40, very talented entrepreneurs, not comfortable with multiple technology platforms and the rapid changes they bring.  The role of the translator is to use domain knowledge in a way that best educates the company.  Knowing the industry is a necessary skill for the analytics translator to ensure that the solution created produces a plan that the business leaders can understand and execute on, and be adopted by everyone in the organization.

According to the McKinsey Global Institute, by 2026 demand for translators in the United States alone may reach two to four million.  Small business will have to use a scaled-down version or modified version of analytics translators to keep up and remain competitive.

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