The New York Times is running a series on the economic and social challenges faced by small town residents and highlights the case of a small business owner, Donna, who runs a quintessential small business - the diner. Here is a list of her daily anxieties as an SMB owner:
- Why are my receipts going down?
- What lunch special can we offer to clean out the refrigerator?
- Should we buy less perch for our Friday fish fry?
- Can we slide a month on the electric bill?
- Since I don't have health insurance, what else can I cut?
“I’m just going in circles and circles and circles,” the NYT quotes Donna as saying, “And not getting anyplace.” If we distill these questions and remove them from the context of the NYT story, they are quite universal to so many small businesses and all of them can be objectively addressed using data and analytics.
Analytics provides two broad areas of value to any business, large or small. They can be classified into two areas - descriptive analytics and predictive analytics.
Descriptive Analytics: is where we look at data to answer questions about what happened. These are Donna's questions such as 1, 4 and 5. The answer to "why are my receipts going down" may be as simple as inadequate marketing or as complex as a shift in the demographics. Perhaps you competition is aggressively attracting clients using new social media based marketing strategies or perhaps your business is not keeping up with current customer needs. But a more nuanced question, that is similar to 1 is "why are my profits down, in spite of good revenues". In that case, you clearly need to look at several things such as costs or profitability per customer. If you are in a low margin business, your overhead costs may be dangerously high. In fact, one of our customers tries to keep an eagle eye on their manufacturing overhead costs precisely for this reason. One way analytics helps them do it is through a custom analytics app to track manufacturing overhead.
At the very minimum, if a business is not quite facing existential crises as indicated in question 5, it needs to at least have a good handle on what are the business' key performance indicators are to know "what to cut". If you want to know what offerings to cut, you need to be able to rank your current products or even customers. A good way to do this is by using the Pareto Law or the 80 20 Principle.
Predictive Analytics: is the more sophisticated sibling of descriptive analytics, where we look at data to answer questions about what is likely to happen. Donna's questions 2 and 3 very clearly illustrate the applicability of predictive analytics to even the tiniest of businesses. One SMB that manufactures automotive components relies on polymer based raw materials. Their raw material cost is a significant influencer of their overall product cost. They want to answer a question very similar to Donna' question 3, which they do by using cost modeling and cost forecasting. Another variant of question 3 is "which products should we build-to-stock based on what is likely to come up"? Another one of our small business customers is doing precisely this - they are using predicted demand for their key products to decide which ones are likely to spike in the next several months so that they can effectively plan their production operations. The way to do this is through demand forecasting.
Clearly analytics is something that every business can and must leverage in order to be able to compete effectively. But there are some caveats or cautions to pay attention to.
3 critical points to ensure that analytics does not become a "bunch of malarkey"
1. If it does not directly help you make decisions, it is not worth it. Any report based on business analytics/data mining that does not impact decision making is not worth keeping. The best way to ensure this does not happen is to objectively frame the question you want to be answered before you start investing in analytics.
2. You don't need to invest in massive databases or expensive software to deploy analytics. Today there is a profusion of open-source tools and cheap cloud-based tools that can circumvent the need to buy expensive on-premise white elephants. Invest in building a good database and make sure all your data is stored in it by following basic database principles.
3. The hardest part about implementing analytics as a small business is finding and retaining the expertise. But an SMBs needs are not as demanding as that of a larger corporation which needs to host an army of in house experts. Today SMBs can tap into the growing pool of analytics consultants as needed.
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(Top image courtesy: creative commons)