Content. CEO Statement 3. Solving for the Impossible 4. The Challenge 5. Optimizing Results is Complicated 6. Daisy s Theory of Retail TM 7

Transcription

1 THEORY OF RETAIL TM

2 Content CEO Statement 3 Solving for the Impossible 4 The Challenge 5 Optimizing Results is Complicated 6 Daisy s Theory of Retail TM 7 What are Key Products? 8 Implementing in a Retail Environment 9 The Results 10 Our Difference 11

3 CEO Statement To date, the Information Age has been dominated by the proliferation of internet access, web connected devices and the ability to capture and monetize larger and larger data sets. This has resulted in the development of a knowledge based society and a hightech global economy. With Moore s Law continually driving the cost of computing downward, it is now feasible to process all of the data we collect. Further advancements in computing power will create an environment dominated by artificial intelligence (AI) and machine learning: we will quite literally enter the age where the capacity will exist to absorb vast quantities of data and produce actionable outcomes that will assist businesses, governments and people to make better decisions in a more timely manner. At Daisy Intelligence, we have been preparing for this revolution for the last 13 years. Our goal is to use our unique AI and machine learning platform along with our mathematical Theory of Retail TM to assist retailers in dramatically realizing the yet unfulfilled promise of the Information Age. Gary Saarenvirta CEO

4 Solving for the Impossible What are my key products that drive customer engagement and increase sales/profits? Which products should I promote or feature this week in our flyer/ in-store/e-flyer/mobile app/direct marketing promotions? Can I reduce SKUs without negatively affecting related product sales? Can product affinities drive the design of my store layout and increase revenues? Answering questions like these is a weekly activity for retail professionals. However, to effectively and mathematically answer the key product affinity question requires analyzing millions to billions of transaction records in search of multiple billions of cross-relationships between variables. For example, the transaction log data of a modest sized retailer earning $500 million in sales per year with 25,000 to 50,000 SKUs would require analyzing approximately 6x combinations per week, every week, to optimally choose 500 items to promote in a weekly flyer to maximize results: an impossible task for any retail team to understand and analyze. Undoubtedly, there is room for improvement to grow weekly top-line sales if retailers could fully analyze 100% of transaction details on an on-going basis.

5 The Challenge Traditional product marketing methods view weekly promotions as siloed campaigns often based on vendor promotions, inventory levels or even best-guess approaches. More advanced organizations may employ enterprise wide data warehousing solutions and query based software to sample data or create dashboards that organize data in a seemingly logical manner to assist in making better marketing decisions. This can help but these approaches cannot optimize results. Oftentimes, a campaign is deemed successful if the sale of the promoted product has increased. Most campaign analysis show week after week of successful campaigns with staggering ROI but the company does not observe an increase in overall sales or profits commensurate with marketing campaign results. The flaw in most modern retail marketing methodology is that it does not take into account variables in purchasing behaviour such as product cannibalization, promotional cadence, seasonal impact and key product affinities because these occurrences are simply too esoteric or mathematically complex to solve. Both cannibalization and cadence have an offsetting impact on promotional results, which is why many apparently successful product campaigns result in minimal impact on top-line results.

6 Optimizing Results is Complicated Successful retailing is much more complicated than an individual product or category view. To predictably grow top-line sales and gross margin, a series of mathematical relationships defining purchasing behaviours must be considered. Furthermore, external factors like competitive response to promotions and pricing as well as market demand have an impact on retailer performance. Trying to analyze billions of transaction records and correlating these records based on key product affinities, basket maximization, cannibalization effects, promotional cadence, price and seasonality to optimize promotional designs was literally mathematically impossible. Until now...

7 Daisy s Theory of Retail TM Daisy Intelligence has developed a mathematical Theory of Retail TM, which utilizes the most advanced and adaptive machine learning models to answer optimization questions. Daisy s Theory of Retail TM is designed to maximize total revenue and gross margin, not just the outcome of a specific product promotion or marketing effort. This model looks at the impact of cross-category cannibalization, promotional cadence, associated product affinities (which products are purchased together and which products are the drivers of affinities), price sensitivity and seasonality. It is complicated but Daisy s Theory of Retail TM makes this possible. KEY PRODUCTS Which products in my stores increase the sale of associated products ( associated sales ) and drive an increase in the average customer transaction size? ASSOCIATED PRODUCTS Which products in my stores are purchased as a result of the purchase of key products? PRODUCT CANNIBALIZATION Do the sales of a promoted product offset either partially or completely due to declines in related product sales within a category or cross-category? PROMOTIONAL CADENCE Are sales of a promoted product offset by a decline in future sales of that product? PROMOTION Which key products should I promote or feature this week in our flyer/in-store/e-flyer/mobile app/direct marketing promotions to maximize total revenue? MEASURING OUTCOMES Did placing, promoting or featuring my key products at the right time grow overall sales this week?

8 What are Key Products? Understanding and leveraging key products is the foundation of Daisy s unique Theory of Retail TM. Key products are those products purchased, which drive the resulting purchase of other complimentary or affinity products. As such, key products drive the overall size of a customer s basket. Some key products are quite obvious, however, some require incredibly deep discovery of purchasing behaviour over time to uncover. The source of product affinities is the customer s intended use of the product ( use case ). Some products complete a use case by themselves. For example, if a person is thirsty a single bottle of water is all that is required. Inversely, some products do not complete a use case by themselves. For example, a can of paint alone is not sufficient to paint a house. A person is likely to buy paint brushes, rollers, drop sheets etc., to complete the task. In the first example, a bottle of water could be part of the use case for lunch (i.e. a person buys a slice of pizza and a bottle of water). The key product in the lunch example is the pizza slice because this is usually purchased with a drink more often than a drink is purchased with a slice of pizza. Knowing a retailer s key products to optimize promotional spending and space planning is the first step towards growing top-line sales.

9 Implementing in a Retail Environment 4 Simple Steps 1 First, provide Daisy with 2+ years of point of sale data, which is input into our Theory of Retail TM and machine learning model. 2 Our machine learning models, running on 100 s of processors, thoroughly analyzes 100% of a retailer s data and provide weekly key product, price and inventory recommendations. 3 Review and implement the recommendations into the operations and promotional mix. 4 By promoting key products at the right time, customers will buy more related products increasing their average basket size. Retailers realize a lift in sales, margin and transactions by implementing Daisy s recommendations.

10 The Results We were amazed. When we listened to Daisy s recommendations, we had an 88% probability of having an above average flyer week, vs. having an 11% probability without Daisy s recommendations. - A Multinational Retailer Daisy s promotion recommendations produced an average uplift of more than $10 million in incremental weekly sales for us nationally. - A Multinational Retailer Daisy s Theory of Retail TM Delivers: Repeatable processes with greater certainty in results. >1% to 5% lift in top-line sales. Better results from every promotion. More knowledge to make better product promotion and placement decisions. And More... When Daisy is analyzing 100% of a retailer s transactional data, other value added outcomes can easily be implemented to further improve sales results. Optimized store layout. Promoted product inventory management. Improved customer experience and engagement. Optimized SKU rationalization process.

11 OUR DIFFERENCE Daisy has a proven Theory of Retail TM to optimize marketing results (read our whitepaper on machine learning to learn more). State of the art: Scalable machine learning technology, able to analyze all factors affecting overall sales, margins and transactions. Simplicity: No system integration or changes to current operations up and running in 21 days. Actionable Recommendations: Not just dashboards and visualizations but personalized recommendations. 5 Measurable ROI: Verifiable ROI based on optimizing total sales and margins.

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