Cost-effective data analytics for smaller retailers

Data analytics are two words that could either evoke fear or excitement to a business owner, depending on which stage of digitalization they’re at. 

In 2022, if your online business hasn’t quite yet implemented data analytics, you’re missing out on a huge amount of potential to drive rewards in marketing and sales.

And yes, as with any other technology or digitalization initiative, there will be some financial cost involved, more often substantial than not.

So what about primarily brick and mortar businesses without much of an online presence? What about the cash-strapped or lean micro SMEs or mom and pop shops in emerging or developing regions struggling to survive amidst competition from the big players?

What if we told you that data analytics isn’t out of reach for this segment as well? 

In this interview with Kaushik Sriram, Partner at Kearney, Tech Wire Asia explores how this segment of retailers can find cost-effective ways to kickstart and/or improve business intelligence with data analytics.

The low down on data analytics 

There’s no shortage of content out there on how to “leverage data analytics” for “impactful insights” alongside scary and expensive-sounding “AI” or “Machine Learning”. That’s how it’s usually packaged, isn’t it?

But at its core, the principle behind data analytics is just simply to use data you’ve collected in a way that drives better decision-making at all points of the business chain. 

Different companies will require different kinds of data. Set an end goal for your business that data can help with. From there, you can figure out what kinds of data you will need and the kind of data analytics services that would be useful to you.

The key to unlocking the large number of potential answers hidden in (frequently) easily available raw data lies with data analysis.

Asking the appropriate questions and combining them with the proper technologies may pave the road for improved data insight and decision-making, hence gaining a competitive edge.

“For example, if a bigger company wants to scale up to capture a bigger market, they can use a data analytics service called location analytics (or location intelligence) to build a view of demographics, population, density, and profile of people in specific locations in an area, region, or country.

“It is a more convenient way to find out the profile of customers. Eventually, find out what use case works for you, and curate it.

“Actual model building would then be much easier”, shared Kaushik.

Data analytics — the cost-effective way

Kaushik mentioned that technology is not the only instrument to collect data — a cheaper method of collecting data would be conducting surveys at the desired location.

This can be carried out with “low tech” pen and paper, or with QR codes to an online survey plastered on the walls.

Data about customers such as age, food preferences, and financial capability can be collected and further analyzed to identify the traits and preferences of customers. A survey can be tailored according to the key data points that you need.

For example, a small food establishment in a university town might find that customers there tend to prefer bite-sized snacks over bigger meals, and expect food outlets to be open long beyond 10 pm on a weekday.

A small-scale home bakery providing home delivery might want to find customers that are close to the area of the bakery. 

Collecting data such as preferred bakery products and financial capability of the potential customers in that area help to determine what are the products the bakery should focus on and set an affordable price range.

“Retailers can also focus on point of service (POS) and stock-keeping unit (SKU) to capture their order value. It is also generally good to invest in customer relationship management (CRM) tools that track transactions and set targets for customers that you want to target.” 

“On a larger scale, the main issue would be data storage and building machine learning models or data platforms for data analysis,” he added.

For SMEs, there are local and global data analytics services with tiered levels which can help to scale up business data collection and analytics needs.

Additional strategies to consider

There are a lot of tools that can be difficult to figure out. So, one way to get more information would be to acquaint oneself with trade associations. Those, or, retailer associations, sharing, and forums can be useful to help individual retailers, he shared.

“For example, governments would have departments that have identified the direction of digitalization for SMEs.

One would be the Malaysia Digital Economy Corporation (MDEC), which launched a set of digitalization guides for SMEs last year.”, added Kaushik.

Taking advantage of these resources, as well as networking, would help SMEs get discovered by other larger companies. 

Other strategies such as customer retention and loyalty can be used to collect data. For B2B SMEs, allowing purchase on credit from smaller businesses or even is a good way to collect data too.   

“Offering credit could grow businesses well. The retail market during COVID times is a big differentiator — so this is where BNPL (buy now pay later) players do it on a big scale. The same concept is applicable for SMEs.

“Profiling customers serves two purposes — to get a better picture of your customer, as well as track their information and prevent fraud. All these do not require fancy tools, but organized and clear systemizing”, added Kaushik.