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How to Solve the Most Common Data Problems in Retail

Posted: 30 March 2017 | By Florian Douetteau, Dataiku

In the retail business, big data is poised in the coming years to open up huge opportunities in the way stores (both physical and online) fundamentally operate and serve customers. Given the incredibly small margins, Big Data will also provide much-needed efficiency improvements – from tighter supply chain management to more targeted marketing campaigns – that can make a big difference to a retail business of any size.

 

Making data-driven decisions is no longer about learning from the past; it means making changes to the business constantly based on real time input from all data sources across the organisation. Making predictions and applying machine learning is based on traditional data but also on new and innovative sources like connected Internet of Things (IoT) devices and sensors or, going a step further with deep learning, unstructured data from things like static images or cameras monitoring stock in warehouses. Consumers can be fickle, so being able to accurately anticipate what they will do next and quickly react is what puts the most innovative and successful retailers above the rest.

 

Dataiku, recently explored the types of data problems facing retail, the problems they solve, and the steps that any retail organisation can take to become more data driven.

 

PROBLEM #1: Siloed, Static Customer Views

Many retailers still struggle with siloed data – transaction data lives apart from web logs which in turn is separate from CRM data, etc.

 

SOLUTION: Complete, Real Time Customer Looks

Cutting-edge retailers look at customers as a whole, combining traditional data sources with the non-traditional (like social media or other external data sources that can provide valuable insight).

 

RESULTS:

  • More accurate and targeted churn prediction.
  • Robust fraud detection systems.
  • More effective marketing campaigns due to more advanced customer segmentation.
  • Better customer service.

 

 

PROBLEM #2: Time Consuming Vendor & Supply Chain Management

Supply chains are already driven by numbers and analytics, but retailers have been slow to embrace the power of real-time analytics and harnessing huge, unstructured data sets.

 

SOLUTION: Automation and Prediction for Faster, More Accurate Management

Combine structured and unstructured data in real time for things like more accurate forecasts or automatic reordering.

 

RESULTS:

  • More efficient inventory management based on real-time data and behaviour.
  • Optimised pricing strategies.

 

 

PROBLEM #3: Analysis Based on Historical Data

Looking back at shoppers’ past activity often isn’t a good indication of what they will do next.

 

SOLUTION: Prediction and Machine Learning in Real Time

Instead, real-time prediction based of current trends and behaviours from all sources of data is the key

 

RESULTS:

  • Anticipating what a customer will do next.
  • A more agile business based on up-to-the-minute signals.
  • The ability to adapt automatically to customer behaviour.

 

 

PROBLEM #4: One-Time Data Projects

Completing one-off data projects that aren’t reproducible is frustrating and inefficient.

 

THE SOLUTION: Automated, Scalable and Reproducible Data Initiatives

The best data teams in retail focus on putting a data project into production that is completely automated and scalable.

 

RESULTS:

  • More efficient team that can scale as the company grows.
  • With reproducible workflows, the team can work on more projects.

 

While each organisation is different, data challenges are the same.  It takes a data production plan to guide any sized team to successfully producing a working predictive model that yields meaningful insights for the business.

 

 

How to Complete any Data Project in Retail

 

The most successful retail companies worldwide solve these four issues by efficiently leverage all of the data at their fingertips by following set processes to see data projects through from start to finish. They also ensure those data projects are reproducible and scalable so the data team is constantly able to work on new projects vs. maintaining old ones. This is as easy as following the seven fundamental steps to completing a data project:

 

  1. DEFINE: Define your business question or business need: what problem are you trying to solve? What are the success metrics? What is the timeframe for completing the project?
  2. IDENTIFY DATA: Mix and merge data from different sources for a more robust data project.
  3. PREPARE & EXPLORE: Understand all variables. Ensure clean, homogenous data.
  4. PREDICT: Avoid the common error of training your model on both past and future events.  Train only on data that will be available to you when a predictive model is actually running.  Choose your evaluation method wisely; how you evaluate your model should correspond to your business needs.
  5. VISUALIZE: Communicate with product/marketing teams to build insightful visualisations.  Use visualisations to uncover additional insights to explore in the predictive phase.
  6. DEPLOY: Determine if the project is addressing an ongoing business need, and if so, ensure the model is deployed into production for a continuous strategy and to avoid one-off data projects.
  7. TAKE ACTION: Determine what should be done next with the insights you’ve gained from your data project.  Is there more automation to be done? Can teams around the company use this data for a project they’re working on?

 

There is no doubt that data science, machine learning, and predictive analytics combined with Big Data will become an even more fundamental part of both online and traditional retail in the coming years.  All retail organisations will use it, but only the successful ones will have an effective data production plan that yields the most effective insights into their business that gives them an edge over the competition.

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