In the first part of my blog on Real-Time Decision-Making (RTDM) highlighting retail industry use cases, we discussed how combining existing historical data patterns with disparate new sources of data completes the Common Operational Picture (COP). To illustrate the use case, we used an Actian customer, Kiabi, and how they used RTDM strategic capabilities to enhance their customer loyalty program.

In the second part, we explored how different roles and responsibilities can use the COP in business-as-usual scenarios versus periods of market disruption using another Actian retail customer, LeRoy Merlin. LeRoy Merlin wanted to extend their decision-making in a data-driven fashion down to managers in their retail locations across Asia, Europe, Latin America, and Africa with an emphasis on sales performance data by-products for a range of factors.

The key points I was hoping readers would take away were that in times of market uncertainties, enhanced customer focus requires business agility that can only be achieved by proper use of the COP to deliver situational awareness based on role and proximity to the point of action. The further downstream you’re able to push the analysis and decisions, the better the results – provided you can balance speed and accuracy.

It Takes Two to Make a Thing Go Right

OK, it’s an old party song stuck in my head, but the sentiment is spot on. One major theme of leveraging the COP to deliver more accurate, fresh intelligence down to frontline decision-makers on the ground is to ensure the focus on customers keeps them happy and satisfies their needs. But it’s also about satisfying the needs of the business. With Kiabi, they wanted to make sure their loyal customers got exactly what they wanted by monitoring their buying behaviors to predict what marketing programs would incentivize loyalty program members to buy more apparel. With LeRoy Merlin, they wanted to empower their store managers to understand locally which inventory was moving and which was just sitting on the shelves to determine how to improve sales performance in that individual store.

Across both these use cases, we’re leveraging RTDM intelligence to drive existing programs and operations to yield better business outcomes. Satisfaction for customers is measured in many ways, with the most critical from a business standpoint: repeated profitable business. During periods of market uncertainty, demand fluctuates, and so does the cost of goods and services. Organizations must ensure they can handle constituents – customers for businesses, patients if we’re talking healthcare, and students if we’re talking education, in a way that avoids unexpected costs or risk. In other words, profitability must be maintained. Even if we’re talking non-profit, operational expenses must be covered for the mid-to-long term. In summary, the thing that needs to go right is the relationship on both sides – for the customer and the business.

Balancing Customer Response and Risk

For several years now, we’ve been supporting The AA, the leading provider of roadside assistance services in the UK. In addition to roadside services, The AA uses independent insurance brokers who work with a group of AA underwriters to offer a range of vehicle and home insurance policies. Actian has helped The AA with its RTDM capability. The AA uses the Actian hybrid database solution to analyze insurance applicant-supplied data against third party data and verification services to assess risk that is critical to quoting a competitive yet profitable policy.

In this case, the COP consists of internal but fluid actuarial data, fraud detection data and models, external sources to collect verification of prior applicant driving records and claims, and relevant demographic traffic accident and property crime rates by location, and so forth. Decision-making is pushed down to the frontline underwriters in that they are assigning risk and to the independent brokers in that they are providing the quotes. However, their roles are essentially as the feedback loop on risk assessment and quoting that is automated, and the interaction with prospective policyholders takes place on competitive Insurance websites like GoCompare.com and CompareTheMarket.com. Prospect expectation and competitive table stakes dictate that all quotes be delivered side-by-side in under a second.

The Actian Data Platform was selected to support the risk assessment and quoting operation because of performance requirements in two separate areas.

1. To meet the speed of collecting the information internally and externally to generate quotes in one second or less.
2. The speed at which fresh data can be visualized for the underwriters in Looker, enabling them to tweak the risk-based decisions based on the competitive landscape.

Real-time is in the “Eye of the Beholder”

In The AA use case, speed and accuracy are both important. Driving record data changes all the time. There is no point in underwriting on a clean driving record based on yesterday’s data when today’s data says the 16-year old daughter just got her learners permit. In other words, if you were to use a Cube to retrieve the data to meet performance requirements depending on the business requirements, your data may be stale, and so the speed is really only one part of real-time, the other part is the freshness of the data.

Both speed and freshness are the true definitions of real-time. The requirements for The AA were 1 second, but for LeRoy Merlin, they’re daily. For many businesses, the real-time requirement is weekly. For example, grocery stores may need to review sales at each supermarket weekly as part of a regular resupply process, and the speed may be an hour or less for populating the data across all stocked items, but the stock data before store managers show up Monday morning for work. In this scenario, stock data doesn’t need to be updated every hour, but perhaps once per week.

During periods of market uncertainty, either or both speed and accuracy may require change. Take the grocery store refresh rate of a week for their stock data, and use of a cube may be too coarse when you have panic buying across everything from pasta to peanut butter and its rolling across different products by day. At that point, your need for fresh data and your real-time requirement change from weekly to daily, and the speed of data collection, analysis, and visualization may drop from an hour to minutes.

In The AA’s case, their real-time requirements are already set on speed and accuracy, and their RTDM capability for business-as-usual easily translates to scenarios where business disruption is taking place.  For many organizations, this is not the case, and the question really is, how do you ascertain what your speed and accuracy requirements are for periods of market uncertainty? In our next blog in the series, we’ll look at what’s needed for speed and accuracy … on a budget. Until then, find out how The Actian Real-Time Connected Data Warehouse can help you achieve your RTDM goals.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

In my last blog on Real-Time Decision-Making (RTDM), I shifted from a theoretical discussion of what RTDM is to use cases that highlight key aspects of the value it can bring to business in periods of market uncertainty. I’m using the retail industry because it’s an industry everyone knows something about coupled with the fact that it’s one of the sectors most impacted by our current business disruptor, COVID-19.

Further, I used Kiabi, a Global retailer headquartered in France, as the use case to delve into the importance of combining existing historical data patterns with disparate new sources of data to generate RTDM insights for a strategic capability. In this case, Kiabi’s customer loyalty program. The key point I wanted you to take away from that example is that in times of market uncertainties, enhanced customer focus requires business agility that can only be achieved by a complete, common operational picture, and that requires additional pieces to the data puzzle.

Democratizing the Common Operational Picture

I briefly mentioned that the Common Operational Picture or COP has several stakeholders and users who may need to leverage the COP in different ways suited to their specific roles, responsibilities, and evolving needs during periods of instability. Making current and accurate information available to and between parties is critical and can make all the difference in service. Take, for example, the old days when you needed a taxi, you’d call the dispatch center, the center would send you a cab, and the cab dispatcher would call you when the cab arrived.

If you got lost and couldn’t make it to the pick-up point, they wouldn’t know. If the cabby got lost, you wouldn’t know. If you were running late, they wouldn’t know and just leave. Along came Uber and Lyft and, now you and the driver know precisely where you are at all times, and you have multiple means of immediate communication with them – the middleman is the app, and it delivers the COP.  In other words, you have different roles – driver and fare-paying passenger – but you have a COP and a means of leveraging it to make decisions and act on them independently of any third party, in a peer-to-peer fashion.

In modern integrated online and brick-and-mortar retail, you want a COP around stock availability and location (specific stores and online) for your customer-facing employees – online, in call centers, at stores – and customers. Under normal circumstances, it would be great to know before you make a trip to a store if the item you want to purchase is there. Now it’s critical as we try to limit trips and their duration to reduce the chances of infection rates going up with COVID-19. A less life-threatening but critical business requirement as well such that you don’t risk frustrating customers who remain dissatisfied long after this period passes.

Decentralization of Decision-Making

In periods of market uncertainty, the issues may be more complicated than is something in or out of stock, and historical data based on business-as-usual stock depletion and reordering patterns will only get you so far. You may need visibility into daily or even hourly buying patterns across your stores – and not just at the corporate level. As an individual store manager, you may even need visibility into your vendor’s stock and the ability order from them or trade with other stores.

Actian helped another retailer, LeRoy Merlin, build out an RTDM strategic capability targeting their individual stores. The capability they wanted to enhance was the ability to monitor sales performance as a function of product SKU, time of purchase and quantity, stock on hand, and several other factors. LeRoy Merlin is a Do-It-Yourself home improvement retailer with over 400 stores in 12 countries across Europe, Asia, Africa, and South America. Their enterprise data warehouse (EDW) was simply too slow to provide analytics services to high volumes of concurrent interactive users across tens of thousands of products. With Actian Vector (the engine inside the Actian Cloud Data Warehouse), LeRoy Merlin was able to perform daily uploads from their EDW to provide real-time ad hoc queries and reporting by up to 2,000 interactive users. The intelligence from the Actian solution enables individual store managers to determine what’s selling and what’s sitting on the shelves so that they can adjust stock – critical during periods of rapid change in demand as we’ve seen with COVID-19.

But it doesn’t have to be COVID-19, for example, DIY retailers in the US can tell where they will run out of stock on Generators by hurricane trajectory forecasts. The key point here is instead of a small group of decision-makers in HQ determining what stock orders should be made and sent where on a quarterly or annual basis or scaling that team up to reduce the hindsight view down to months or weeks. The RTDM capability provides LeRoy Merlin the ability to do it daily at the individual store manager level, decentralizing decision-making to improve speed, accuracy, and business agility.

Dynamic Pricing and Dynamic Rationing

As was the case with the RTDM strategic capability Actian supported for Kiabi, the support for LeRoy Merlin can be used for more than business agility to change stock and improve sales performance. It’s not uncommon for store managers to have the latitude to discount items that aren’t moving off the shelves. For this solution, the business-as-usual requirement we helped Kiabi with was speed. They couldn’t get back real-time reports and queries to so many people at the same time. It was not an accuracy issue or in other terms, the freshness of the day you could use the sales performance data the Actian solution is providing would certainly help them make the right decision about this, but the data necessary to decide what to discount or what to reorder is probably not stale if it’s refreshed on a weekly or even monthly basis.

In a period of market uncertainty that generates a rapid change in demand, you still need the real-time response, the speed, but you also need accuracy, current data. With panicked customers, you may need to reorder immediately, or you will see an empty shelf and, before your shelves empty of existing stock, you may need to adjust limits on purchasing quantities and even adjust those limits on a daily basis and let customers know before they make a trip to the store. Those limits may need to be different at different stores, again leveraging those closer to the transaction to make the decision.

Capability Reuse

You may even look to leverage the RTDM capability you’ve built across multiple core business processes. Everything we’ve just discussed could be combined with your loyalty program, and you could send out notifications stating when new deliveries of toilet paper will arrive and what the purchasing quota will be, setting expectations in advance.

Finally, as I mentioned last time, RTDM capabilities are needed in almost any industry, and virtually all industries are impacted in some way by business disruptions. In the next blog, we’ll start to gradually shift to a discussion of use cases in other industries and what it takes to build a world-class RTDM capability.

In the meantime, learn more about RTDM and Actian here.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.