Real-Time Decision-Making Use Cases in the Retail Industry – Part 3

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.

Im zweiten Teil untersuchten wir anhand eines anderen ActianKunden aus dem Einzelhandel, LeRoy Merlin, wie unterschiedliche Rollen und Verantwortlichkeiten den COP in Szenarien mit normalem Geschäftsverlauf und in Zeiten der Marktstörung nutzen können. LeRoy Merlin wollte seine datengestützte Entscheidungsfindung auf die Manager seiner Einzelhandelsstandorte in Asien, Europa, Lateinamerika und Afrika ausdehnen, wobei der Schwerpunkt auf den Nebenprodukten der Verkaufsleistungsdaten für eine Reihe von Faktoren lag.

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, es ist ein altes Partylied, das mir im Kopf herumschwirrt, aber die Stimmung ist genau richtig. Ein wichtiges Thema bei der Nutzung des COP zur Bereitstellung genauerer, frischer Informationen für die Entscheidungsträger On-Premises ist die Sicherstellung, dass der Fokus auf den Kunden gerichtet ist, um ihn zufrieden zu stellen und seine Bedürfnisse zu erfüllen. Aber es geht auch darum, die Bedürfnisse des Unternehmens zu erfüllen. Bei Kiabi wollte man sicherstellen, dass die treuen Kunden genau das bekommen, was sie wollten, indem man ihr Kaufverhalten beobachtete, um vorherzusagen, welche Marketingprogramme die Mitglieder des Kundenbindungsprogramms zum Kauf weiterer Kleidungsstücke animieren würden. Bei LeRoy Merlin sollten die Filialleiter in die Lage versetzt werden, On-Premises zu erkennen, welcher Bestand sich bewegt und welcher nur in den Regalen liegt, um zu ermitteln, wie die Verkaufsleistung in der jeweiligen Filiale verbessert werden kann.

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, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.