Many practices tend to overlook the small business market when exploring analytics opportunities for reasons ranging from price sensitivity on the consumer side to an impractical cost to deliver on the side of the provider. This paper proposes that although not all small business models are viable prospects, there are some that may offer ripe opportunities in light of their size due to the specifics of their analytics challenges and the limited investment required to address them. Is a $10,000 engagement viable, can it be delivered with OOB tools like Alteryx or ubiquitous tools like MS Excel and be a lucrative niche offering? I have yet to prove it, but on the surface, I believe it can.
The key variables that determine the viability of a small analytics engagement with a client are identical to those of larger clients at the highest level. They are the maturity level of the client, the analytical problem type, and the quality and availability of data.
Maturity Level
The opportunities to apply analytics to address business problems are available at all stages of a business’ life cycle. However, those at the beginning which include market identification and customer base acquisition are daunting in that they would require a significant amount of 3rd party data as well as time at the onset which may be financially prohibitive to a new business owner. For this reason, I propose that the target audience should be the more mature business owner with a core customer base whose priority is growth and is looking for opportunities to address this growth with a more “intelligent spend” as in targeting the spend whether it be marketing or client support in such a way that the return is quantifiable and adjustable.
Problem Type
The ideal business model that comes to mind is that of an agency offering financial services or insurance products. These types come to mind because the relationships between the proprietor and the customer tend to be more interactive and frequent and those two variables provide the tremendous amount of data collection opportunities. Additionally, good data will drive better analytics and thus improves the likelihood of a tangible, quantifiable result for your small business client. Although new customer acquisition is always a priority, so is understanding who has growth potential, who is an attrition risk and where her time and resources should be targeted to maximize the value of her customer base. This type of problem is the most common in the real world from an analytics toolbox perspective in that it lends itself to the application of Classification methods since the goal of the business is a segmentation model that would allow the alignment of resource spend (marketing, value add services, etc.) with the clients’ value to the firm (current, potential, and lifetime). In other words, aligning a segmentation model with a cost to serve model. Classification methods abound, but your client may not be that interested in the science of some of the more complex methods available and more than likely needs you to apply an easily explainable (logical to the layperson) methodology. In the case of the client having an idea of the number of segments they have identified from heuristics, K-Centroid, Hierarchical Clustering or Principal Components Analysis may be viable approachs. Of course, your analyst may have a differing opinion on the method to use, but the key to remember is that your client needs to feel empowered, not overwhelmed, so the ability to explain your reasoning and process should be one of your core focuses.
Data Quality and Availability
The client business model that I have proposed is ideal because of its 1:1 interaction between the proprietor and her clients. This allows for the capture of specific and robust historical data about their accounts including policy changes, portfolio changes, assets under management, meetings. Additionally, a major plus of this business model is that unquantified (yet valuable) data attributes are also easier to capture such as ‘high’ vs ‘low’ maintenance or family demographics that could indicate future product or service needs, a key component of your final model. As a matter of fact, this will be where the majority of an analysts time will be spent, quantifying the “unquantifiable”. Turning ‘gut feelings’ into attributes and working with the client to think more broadly about their experience with a client and the client’s experience with them that they probably have had time to in the past. The benefits are real however if this exercise allows a business owner to integrate a cost to serve variable into their pricing proposals or their policy/account reviews. The guilt of spending too much time here or not enough there is eliminated and now reflected in their core KPIs such as margins, attrition rates, referral rates, etc.
Conclusion
What I have described thus far is a very common and often unaddressed challenge being faced by small business owners. However I propose that the execution of this approach can be accomplished with minimal investment by executing the following steps:
1) Planning discussions with client: 4 – 12 hrs
2) Data acquisition and transformation: 8 – 24 hrs
3) Identify and quantify the “unquantifiable” attributes: 4 – 8 hrs
4) Method selection and model training: 4 – 8 hrs
5) Model testing and review: 8 – 16 hrs
6) Productionalize model: 16 – 24 hrs
This engagement when modeled provides an estimate of about 80 hours with 90% confidence, which equates to ~$10,000 at $120/hr. Quarterly or semi-annual model reviews and fine tuning could net additional revenues. I welcome your thoughts and feedback.
