How Circle K is embracing data-driven transformation: Sushant Bhushan, Head of Analytics Delivery

Circle K is a global brand, and its network consists of convenience stores across 26 countries and territories spanning US, Canada, Europe and Asia. Circle K is Fortune 200 company and has over 1,30,000 employees across the globe. The company is expanding actively in India and has recently opened a new global capability center wherein it is looking to expand its analytics and data science team. Sushant Bhushan, Head of Analytics Delivery, Circle K recently spoke to Dataquest about how the company is undergoing a data-driven transformation.

DQ: Where Are You In Your Analytics Journey?

Sushant Bhushan: I’m not going to sugarcoat it. Diving into the world of data analytics is a big, huge step, but if you want your business to not only survive, but thrive, it’s a necessary one. Your journey should begin sooner, I would suggest, rather than later. This is a big cultural change and can completely change your mindset from anything you’ve ever known about business.  Leveraging your data goes beyond implementing tools. It is a journey your organization will take to understand what is important to measure and how you use that information to drive improved decision making. We saw this massive change in our organization! Being a 42 year old organization we realized power of data very recently. I am proud of what we achieved till date and about our plans of taking it to greater height. 

Data maturity and organizational transformation go hand in hand; an organization that gets better at harnessing data will see transformation in its people, processes and overall business results. The word mature can mean “having reached the most advanced stage in a process.” Data analytics maturity happens when an organization advances how they leverage key information to make critical business decisions. At Circle K we are living these values keeping data at the core of every decision we are making - be it critical, strategic or tactical. Business started aligning on few questions such as - How does the data align to our strategic goals? Does it drive revenue or reduce cost? 

While sales is usually where a company focuses its data-improvement efforts, sometimes a “champion” to support change works elsewhere in the company – in finance, marketing or operations. When an organization demonstrates that it can add value by using data to make decisions in one area, they can push change into other areas. Data maturity at an organization is viral. Once one part of the business buys in and builds the discipline and good habits of running as a data-driven organization, that discipline can be instilled into other parts of the organization. 

The COVID-19 pandemic unleashed an unplanned stress test on most organizations and on how well they could pivot operations based on visibility into their data. It was a catalyzing event that forced organizations to examine their operations and make decisions much quicker than in the past.

For big companies like us in convenience and quick service restaurants, needed to make real-time staffing decisions based on demand shifts. Sellers of pandemic related items, especially sanitizer and mask saw product fly off shelves and out of warehouses. When the pandemic lockdowns started around mid-March, if companies had to wait for month-end results to make decisions, it would have been too late for them to pivot effectively.

Getting good at using core data has become table stakes for successful companies. Whether because of this pandemic or some other black swan event in the future, if an organization can adjust its business based on what it sees in its data, and a competitor cannot, their ability to move faster will outcompete everyone.

DQ: How Do You View the role of AI and ML Models when it comes to the retail industry?

Sushant Bhushan: The retail industry is undergoing a continuous evolution on every front – customers are constantly changing their purchase patterns, the market is moving towards becoming complex ecosystems. The emerging technologies are disrupting the sector at a stunning pace. Meanwhile, the shoppers are being bombarded with luring offers competing for their attention on every channel, from online (web to mobile apps) and in-store.

By combining Machine Learning with marketing efforts, organizations can make the best use of their consumer data. AI functionalities like computer vision, visual search & NLP are proving to be game-changers by improving optimization & forecasting for the retailers.

Since this sector has a wide range of applications, there are many use cases I could discuss. With the use of this technology, operational efficiency may be increased, inventory costs can be reduced, and retail operations can be modified to response to future changes in the market. Here's how to go about it:

• Demand Forecasting and Stocking

Demand forecasting allows you to make significant changes to your product's pricing, positioning, promotion, and sales strategies. For instance, data scientists can create models that use time series, regression, and historical data to forecast predicted sales of particular products over a given period of time. Analyzing customers, the product, and the competition can help with price decisions. Additionally, by using inventory and supply chain data, it is possible to keep adequate stock levels, predict future inventory demands, and ensure the ongoing availability of in-demand products.

• Making Profitable Pricing Decisions

It might be difficult to decide on the appropriate product pricing and to adjust it in response to consumer preferences. The majority of sellers prioritize seasonal trends, demand peaks, and pricing strategies used by rival businesses when making decisions. But a lot of other things can affect the cost. Machine Learning can be used in the retail sector to develop algorithms that can help determine whether it is appropriate to start lowering prices or raising them. These can monitor inventory levels and competitor prices, compare them to demand, and determine the appropriate prices.

• Ensuring a Seamless Supply Chain

The data that ML algorithms are based upon can be extremely helpful in choosing delivery routes. Smart systems guarantee efficient logistics and accomplish two objectives at once: improved customer service with quick delivery and lower overhead costs. However, it is crucial to make sure that the data is free of errors and inconsistencies in order to profit from ML. Making accurate forecasts to make the best judgments and keep consumers happy is made possible by the cleansed data.

• Determining Customer Lifetime Value (CLTV or CLV)

The amount they spend on the offers, their consistency, their payment history, and the number of times they've purchased can all be used to assess which consumers have high lifetime values. Businesses can improve their marketing campaigns with the use of these information. Subsequently, it would increase their share of the most valuable customers and generate a steady stream of revenue.

• Tracking Customer Sentiment on Social Media 

The way that consumers shop has changed thanks to social media, where leading companies are present and active on well-liked platforms. These platforms are also frequently used by brands as an official customer service contact point. This highlights the significance of monitoring brand reputation and customer sentiment on various channels. Machine learning and artificial intelligence have made it possible for brands to monitor their social media presence on a broad scale, evaluate data on customer involvement and sentiment, and understand the factors that influence traffic.

DQ: What do you have to say about the issue of automation taking away jobs?

Sushant Bhushan: It is no longer relevant to argue that recent technology breakthroughs, particularly automation, machine learning, artificial intelligence, robots, and the internet of things (IoT), pose a threat to employment. In fact, growth in orchestration and decision-making has been supported by developments in workflow software, content management, productivity software, business rules management, and current robotic process automation – or, to put it another way, most information technologies. 

With improvements in analytics and AI, including increased automation in the fields of business process management, this trend — of replacing human decision-making and management skills — is accelerating even further. Does that imply that knowledge workers and middle managers would lose their jobs? The response is "no."

So, in essence, automation is not actually taking away jobs. It is only nudging people to perform more fulfilling and progressive tasks. It is allowing businesses to create a more balanced working environment, where people can apply their experience and decision-making skills. Automation, in this sense, is a major boost to knowledge worker empowerment. Work profiles are divided into numerous strata in every industry. People are still forced to engage in monotonous, repetitive chores, most of which are depleting and exhausting. Lower productivity and a demoralized workforce are major roadblocks to the growth and development of businesses. Automation unleashes untapped human potential, prepares businesses to accomplish more and gives knowledge workers more control.