–Retail

2月 162018
 

pricing and promotionThe consumer packaged goods (CPG) and Retail industry are going through a period of significant change. Both retailers and manufacturers are struggling to find growth and improve profitability. One strategy is through consolidation - e.g., Kraft-Heinz, Keurig- Dr Pepper Snapple Group on the manufacturer side, as well as Safeway-Albertsons, Ahold-Delhaize, Walgreens-Rite Aid on the retailer side. The thinking here is that these mergers would lead to large operational efficiencies and focused growth strategies.

Another important lever to drive growth is pricing and promotion. Companies have realized the importance of getting the pricing right and running high-impact promotions in a highly competitive market. As consumer shop multiple channels and new retail formats begin to permeate (e.g., smaller format stores, new entrants such as Aldi and Lidl), the importance of price-promo continues to increase. Pricing and promotion have become the second largest item on CPG manufacturer’s P&L, after cost-of-goods. Similarly for retailers, price-promo decisions have become critical for growth, maybe even survival. This is manifested in the growth in investment focused on pricing and promotion decisions. In some cases this investment could be as much as 20-25% of net revenue of the company.

However, despite the heavy investment in price-promo, the impact of these decisions is declining. A recent IRI study indicated that the price and promo elasticities (response of volume to pricing change) have been steadily declining over the past 3-4 years. Consumers are willing to buy less when faced with decreases in “regular or base” price as well as promoted price.  The study indicated that the “lift” from promotions had decreased by about 1,000 basis points over the past four years.  There is, therefore, an immediate need to manage price and promotion decisions in a more creative and impactful manner.

Three areas of improvement

What does this mean? What can companies do to improve the impact of their pricing and promotion investment? We believe that there are three important areas of improvement. The first area is around a more refined understanding of the impact of price-promo decisions.  The new focus is on understanding the true impact of merchandising through both traditional and new lenses, including stockpiling, cross-retailer pricing and advanced price engines. Being able to more accurately predict the pattern of consumer behavior allows for automation and faster and better decisions.

The second area is around rapid and dynamic decision making. This involves a focus on new techniques such as Artificial Intelligence and Machine Learning to drive price-promo decisions. AI/ML is already getting entrenched within demand identification, product development and in-market execution as well as marketing. Within CPG and retail pricing, this will be accomplished by (a) speed in dealing with the regularly-repeated manual tasks in an efficient manner and (b) new levels of insight and accuracy based upon market trends that enable pricing analysts to focus their efforts on the areas that matter in a dynamic manner. It is imperative to move from a user-driven, manual pricing adjustments to dynamic “smart solutions.”

Another important area of change in pricing and promotion is “personalized pricing;”that is allowing manufacturers and retailers to customize price-promo decisions towards individual consumer/shopper segments. This is done by combining frequent shopper (FSP) data with traditional price-promo modeling for an in-depth evaluation of merchandising strategies as well as developing custom offers that would stimulate demand within these segments. IRI research shows that FSP/loyalty card holders react differently to brand price changes. For example, Brand Loyals react stronger to base price changes, while Brand Non-Loyals react stronger to base price reductions, promotional prices and quality merchandising tactics​.

In our session titled “New Frontiers in Pricing Analytics” at the SAS Global Forum 2018, we will provide a detailed overview of the state of the industry and how it is evolving. We will provide an overview of the new techniques and technologies in this space as well as where things are headed in the future. We hope to see you there.

 

Shifting sands in pricing and promotion was published on SAS Users.

11月 202017
 

Just last week, Walmart announced that they'll be testing inventory management robots. These robots will cruise store aisles, scanning shelves to identify out-of-stock products and other issues. According Reuters, Walmart is testing these camera-equipped robots in a handful of stores, but plans to expand the test to 50 stores. We [...]

Effective retail planning requires precision and finesse was published on SAS Voices by Dan Mitchell

6月 142017
 

There's been a lot of talk in the media lately about the death of retail. Every week, it seems like another retailer announces the closing of stores, acquisitions or even going out of business. Many relate it to the growing competitive landscape with the convenience of online shopping and lure [...]

Retail -- more alive than ever was published on SAS Voices by Brittany Bullard

3月 152017
 

Omnichannel Analytics are helping companies uncover patterns in big data to improve the customer experience.  Using those insights, companies can anticipate what consumers are planning to purchase and influence that purchase in real time.     Companies are experiencing unprecedented complexity as they look for growth and market opportunities. Their product portfolios are [...]

Omnichannel is changing the way we view demand planning was published on SAS Voices by Charlie Chase

3月 152017
 

Omnichannel Analytics are helping companies uncover patterns in big data to improve the customer experience.  Using those insights, companies can anticipate what consumers are planning to purchase and influence that purchase in real time.     Companies are experiencing unprecedented complexity as they look for growth and market opportunities. Their product portfolios are [...]

Omnichannel is changing the way we view demand planning was published on SAS Voices by Charlie Chase

1月 272017
 

There are so many ways in which a customer’s journey of experiences can be negatively affected, from forms on websites that are unclear or complicated, to inconsistent or non-relevant interactions over many channels. It is important that these interactions are measured and reduced to maximize customer engagement and increase customer satisfaction over the long run.

You can tackle this challenge from several directions, from A/B testing and Multi-Armed Bandit tests that optimize interactions at specific points in a journey (these are available in SAS 360), to approaches that optimize the full customer journeys over many sequential points in the journey.

Optimizing full analytically-driven customer journeys

This year I was involved in a project for a large retailer and the retailer believed there were a significant number of interactions with customers that had an impact on response rates – i.e., positive (halo) effects and negative (cannibalization) effects. These are difficult to deal with using standard optimization techniques that assume independence of contacts, and therefore full customer journey optimization was used to identify these effects and address this complexity successfully.

As always, the first step was to get the consolidated data, at the individual customer level. We were able to accomplish this because we had good, quality data for the project – customer-level demographic data, and contact history data.

The stages of customer journey optimization

The journey optimization was then carried out in three stages:

Stage 1 –Creating analytically driven customer journeys is an important advancement towards truly effective analytically driven omnichannel marketing. We used decision trees (in SAS Enterprise Miner) on the customer history data to map widely varied journeys that customers were taking. Traditionally, decision trees are used on a wider set of data, but by using just the history, the paths of significant activity that led to purchases were identified.

Stage 2 – Next, these analytically driven journey maps were used as inputs for optimization. For every journey identified by the decision trees, a predictive model was created (using SAS Enterprise Miner) to predict spending, so that for every customer, for every journey, we can predict how much they will spend.

Stage 3 – Finally, the data was optimized using SAS Marketing Optimization, and constraints were applied to establish a final set of scenarios that the retailer agreed would be appropriate to implement.

This illustrates how decision trees can be used to map customer journeys, and these journeys can then be optimized; to replace the disjointed and disconnected results of traditional optimization methods. We are also beginning to use the cutting-edge machine learning technique of deep reinforcement learning to further optimize customer journeys. These techniques will be incorporated into SAS Customer Intelligence solutions to ensure that SAS users can explore this complicated and increasingly important area of customer intelligence.

tags: A/B Testing, customer journey, customer journey mapping, multi-armed bandit test, optimization, retail, sas customer intelligence, sas marketing optimization

Customer journey optimization: A real-world example was published on Customer Intelligence.