SMEs are the lifeblood of the Sri Lankan economy. I have been engaged with a lot of SME clients in various engagements in my consulting career. A key finding was that the SME sector does not utilize and get benefits out of the data available to them. Hence, the following writing is a discussion on how data analytics can be used in SMEs, supported by some practical examples from the sector.
External data
SMEs find it hard to know what their customer perceptions are. This could be overcome by analysing external data available. Online reviews are effective in this regard for SMEs to identify what customers are talking about their businesses. Businesses can get a pulse of customer feedback and sustain what is good and improve the gaps. Looking at these reviews, we have recommended contacting customers who write the negative reviews and identify the cause and improve the service accordingly.
ERP data
In terms of internal data, most SMEs are equipped with basic ERPs. However, in the SME sector, these ERPs are not customized to generate the information the business needs. These systems collect a fair amount of information that can be used for the analysis and is beneficial for the business. We have used this data to identify key elements including the most popular products among the customers, most profitable products, profitable suppliers etc. SMEs can use this information to plan their next moves.
Market basket analysis
Although most SMEs have data granular to the customer receipt level, they have no understanding of what customers tend to buy together.
This analysis can be used to identify the relationship between the products which are more frequently bought. Placing the products both physically and digitally closer to each other, enable the consumers to notice them and also do some marketing campaigns like basket discount offers.
Customer classification
Most often SMEs attempt to increase their revenue by placing all customers in one basket. This, however, may not be the best way. To overcome this gap, we have helped SMEs to identify their customers using criteria such as the frequency of transactions, value of transactions and how recently the clients have done transactions.
A loyal customer who does more transactions with a higher transaction value would like to be rewarded for his loyalty. For customers who spend less, some SMEs have introduced tiers of rewards that will be unlocked as they spend more.
Online ordering platform
Some of these SMEs have online ordering portals as well. SMEs very rarely use the data from these platforms for the growth of their business. Data from these systems can be used to identify some key indicators for SMEs to enhance the web ordering experience of the customers. For example, one client wanted to know how many products are ordered at a time by a customer and whether the same customer orders repeatedly using the web portal. Looking at these indicators and taking certain action such as mitigating unpleasant experiences on web portals, resulted in an enhanced use of the web portal by customers.
Geo Mapping
Most SMEs have information related to geo-locations of customers. This could be either district-level information or individual-level information. Ex: the postal address of a customer. Most often, this information is perused on an Excel sheet or a printed paper. This kind of reading of raw data will not enable insights as intensely as visual or graphical data.
Mapping these sales data at a district level/provincial level or at the actual geo-location of customers can indicate where businesses should focus more to increase their sales and help come up with strategies to excel.
Inventory optimization and efficient debtor management
SMEs can also utilize their data to increase efficiency of internal processes. In some engagements with SMEs we have used this data for smooth inventory process operations; identify what products are used frequently and what items are used rarely and come up with strategies to keep their inventory at an optimal level.
SMEs can utilize their data to manage debtors. We have used this data to simply analyse the average number of days to recover debts and the amount recovered and to handle their debtors effectively.
These are only a few instances/examples of where we have used data for SMEs. The scope of data in the SME sector is much broader. This writing envisages to provide a high-level summary of how SMEs can use their data in a meaningful way to impact both businesses and customers.
Gemunu Premarathna
Lead Consultant — Data Analytics at StrEdge
