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Personalized retail marketing using in store location

Par   •  6 Septembre 2017  •  1 454 Mots (6 Pages)  •  95 Vues

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[1] Dwell time: The duration of a visit at particular location. [2] The transition probability from state a to state b in a single step is the one step transition probability Pab, which can be viewed as the fraction of movements from zone a to zone b over the total number of shopper movements from zone a to the other zones

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- Analytics Module 2 - Past Purchased Basket item Analysis:

In this module, we will apply Association rule mining technique. Using A priori algorithm, we will build rules that correlate the presence of one set of products with that of another set of products. Each rule will have values for support, confidence and lift.

- Analytics Module 3 - Web log Analysis:

Web logs can be analyzed in order to gauge the interests and preferences of the customer. We propose to use regression model to estimate the probability of the product that is most likely to attract the customer, based on the web log data.

Model Integration:

The analytics modules can be integrated to send personalized promotions to the customer based on their behavior and preferences. It integrates the results from the analytical models and can be used to build customized marketing message for items of interest.

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While considering the products for personalized marketing, the products recommendations derived from association rule mining module are selected based on highest support, confidence and lift criteria and then compared with the products that are most likely to be of customer’s interest (based on we log). The common products, if any, could be regarded as the ideal products for sending personalized message. These products are further matched with the products in the next most probable path and we select the best match in terms of revenue/margin/benefit/customer satisfaction (depends on business goal). Personalized marketing message can be sent to customer for the selected products.

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Business Benefits

Benefits to Retailers

Benefits to Customers

- Improved Sales

- Personalized Offers

- Cost Savings in Marketing Promotions

- Savings

- Increased Customer Loyalty

- Ease of Redemption

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Conclusion

Using the combination of the three analytical modules retailers can connect with customers and add more value to shopping experience, resulting in better customer targeting, communications, efficient marketing campaigns and special offers. This, in all, will result in increased sales and profitability.

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Recommendations

- To implement the suggested solution, it is imperative that the data is kept separate from transaction oriented application in which it was created. This will help in improving business operations. One way to do this is by implementing an Enterprise Data Warehouse (EDW).

- To monitor system inaccuracy, promotional campaign effectiveness is recommended to be analyzed to fine tune the model for better performance.

- This solution has scope for extensibility. For instance, the output from Association Rule Mining can help a great deal in Marketing Mix Modeling, the Dwell Time Analysis presents opportunities to explore the results for use in store optimization. There are various possibilities that can be explored.

- Retail data security is bigger than just a data security solution because most retail environments are highly distributed. So we recommend the use of professional security management system to get a better handle on IT security and compliance.

- Consumer privacy comes first. It is important to inform shoppers about foot-traffic analysis being carried out. It is recommended to post notices in store highlighting this. Customer opt-in preferences should also be checked before pulling them in any kind of analysis.

- Since there is huge cost associated with the implementation of this solution, it is vital to have support from leadership at all times. We recommend high interaction with management on daily basis to keep their interest levels up.

- Good understanding of analytics concepts is critical. So we recommend either hiring those who possess these skills or providing training to current employees before working on these roles.

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References

- https://www.google.com/patents/US20080249870

- https://www.knime.org/knime-applications/market-basket-analysis-and-recommendation-engines

- http://paginas.fe.up.pt/~ec/files_0506/slides/04_AssociationRules.pdf

- http://siam.omnibooksonline.com/2011datamining/data/papers/WS02.pdf

- http://www.doctornerve.org/nerve/pages/interact/markhelp.htm

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