We know that “big data” is a big buzzword for big business. However, with all of the data sources that entrepreneurs have available, what are the promises -- and potential pitfalls -- for small business owners?
To come up with a strategy and process for small business owners to make the most from big data, I talked to customer-centric, data-driven strategic marketer Marina Erulkar, founder and principal of Hampstead Solutions LLC.
Here are eight steps that Marina says that you should be taking to harness the power of big data for your small business.
To make the most of big data, small businesses must be laser-focused on their intentions and goals, being selective about what they consider, and disregarding the rest. As Marina shared, discipline is key to harnessing the power of big data and without it, it’s too easy to become overwhelmed by the metrics that can be generated. Just because you can measure it doesn’t mean that you should. Some results simply may not be beneficial.
For example, take the vanity metric of “likes” on a Facebook post. Likes are one source of data a business owner could easily capture -- but they don’t necessarily tell you anything. Millions of viewers may have clicked the like button, but never read the content (you know you’ve done it). If those likes are not predictors of any type of success, there is no point measuring them (beyond the feel-good factor).
Companies -- especially new companies -- must set goals. Those goals must be assigned milestones so that you know what success looks like.
A new business may have a near-term customer acquisition goal, for example, but needs a longer-term objective to double the revenue from those acquired customers. With these defined objectives, including their timing, the company may focus on acquisition until it reaches its first milestone and then, focus on customer growth exclusively or in parallel.
“By setting goals, a small business, which necessarily must be selective with how and where it allocates resources, will more efficiently move through its intended growth phases. Objectives also ensure that measurement results are valuable and timely. They allow you to identify required data in advance. If the data you need is not currently present, you will be able to identify sources and build the necessary dataset over time”, Marina notes.
Create a learning agenda so that you will have the intelligence -- and the data that supports it -- in advance of that need.
Building on the earlier example, as that new company shifts from acquisition-only focus to up-selling and cross-selling, they may need to understand what signals a new opportunity from existing customers.
The business has the time to assess its CRM (customer relationship management) systems, ensuring customer interaction is captured and to instruct its client management team to collect and enter certain information. By setting a learning agenda, the business will be ready with metrics and intelligence when they begin the next phase.
Defining and collecting the data that supports anticipated, essential intelligence needs to happen in advance so that progress is not slowed, interrupted, or driven off course.
Marina advocates prudent KPI (key performance indicators) and metric selection, because these numbers will be the basis for significant insights and decisions. Therefore, the selected metrics must describe progress to the intended goal.
For example, metrics supporting customer acquisition goals could include message response (opens and clicks), awareness (website traffic and page view duration), conversion costs (cost per click, cost to acquire, etc.) and, of course, sales (products and revenue).
“Metrics should always be chosen because they deliver essential insight. This requires some discipline, but the payoffs are confident decisions -- and defense against diversion from empty numbers,” Marina notes.
So, if a small business works within a subscription model, they will additionally focus on CMRR (committed monthly recurring revenue) and the renewal rate. For some companies, managing regulatory requirements will influence measurement selection.
Businesses must have confidence in the data that supports their intelligence and decisions. Marina explains that knowing the source, age, and hygiene of the data will ensure confidence in the results.
For example, if sales cycle data includes only converted customers, and does not include unsuccessful or unfinished sales efforts, the dataset is incomplete. Resulting metrics and any projections will be skewed.
Alternately, the data may not be clean. The phrase “garbage-in, garbage out” comes to mind. Sometimes, your dataset might be, well, junk. That could be a painful realization, but it is far preferable to basing decisions of any value on unreliable, flawed data.
Companies should produce their selected KPIs and metrics on a regular basis. Results should be routinely reported so that trends and opportunities may be identified.
“The numbers a business generates are individual to that business. They create competitive advantage. The only way to get to know a business through its numbers is to generate those key numbers in a steady cadence,” Marina says. “Then, through more sophisticated analysis, augment the baseline insights with more specialized intelligence.”
Marina notes that this step is where you reap the rewards of planning, reporting, and analysis. Understanding the implications of measurement requires critical thinking. You must know your business, your objectives, and your numbers in order to be successful at this crucial step. There is no substitution for that.
Looking beyond the obvious is also required here. For example, you may note that response increases with a discount. The apparent tactic is to continue discounting with the expectation of continued response.
But for a small business, a downward pricing spiral can be a death spiral, so examining other factors contributing to the result (such as below-average product feedback that reduces perceived value) may be a logical, and more valuable, next step.
“Reviewing results must be routine to know your business, its health, and performance. Through its numbers, small business can quickly recognize improvement and erosion, commonality and outliers, etc. Measurement quickly becomes insight when continually and critically reviewed. Those insights will sustain informed projections and decisions,” Marina notes.
This is where small business necessarily has an advantage. With sound decisions based on analytic insights, small businesses are in a better position to seize recognized opportunity given their size and the speed with which they can adapt.
Establishing iteration as a process will ensure that small businesses continually improve as data-driven opportunities are recognized.