–Retail

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.

1月 112017
 

Omnichannel shoppers have been disrupting retailers for years, and its likely to top the industry’s agenda of challenges for years to come. But optimization, an omnichannel analytics technology, can help harness the positives of omnichannel retailing and minimize showrooming. Consider this everyday retail dilemma: E-commerce sales are growing, but in-store […]

Retailers use optimization to improve in-store fulfillment and keep customers satisfied was published on SAS Voices.

12月 202016
 

Black Friday 2016 took everybody by surprise. The biggest shopping day of the year is a crucial date in any retailer’s calendar. And rightly so. Auditors and analysts predicted that 2016 would see the majority of consumers splashing out more cash than ever before on everything from scented candles to […]

5 ways for retailers to thrive in post-Brexit Britain was published on SAS Voices.