Customer Analytics

Lets identify who are the customer and what they buy

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How we do it?

Tracking customer
Shopping Journey

Calculating his
purchase behavior

Identifying different group
of different purchase behavior

Grouping customer
based on their spending

What are the Good customers who buy from you and what are the customers keep coming back and encourage others to do the same. What is the difference between each or group of customers?

Why customer get connects with a local store location. Brand stores or a company? Many of business try to find the right answer into different why.

It has become more difficult to track and manage the customer experience. Fabi analytics is able to bridge the gap. drawing in data from shoed data sets, plus external data on demographics, psychographics, share of spending and more to create a more complete understanding of the customer experience through their eyes.

Customer Behavior base on purchase

  • Routine response on their bill size.

  • Extensive decision making on each visit.

  • Attention base on Impulsive buying.

  • Purchase base on social events.

Customer Purchase life cycle

  • Customer visit base on weekly, monthly, quarterly.

  • Drop out customers Analysis.

  • Customer retention measures.

  • Customer loyalty performance increase or decline.

Customer demand based on Season or any events

  • Unpredictable demand spike

  • Product in in high demand based on location.

  • Product mix purchase by location.

  • Event or marketing promotion based performance.

Back out Customer analysis

  • Customer purchase once in 3 to 6 months

  • Number of customer compliances vs customer back out.

  • Customer feed back analysis (NPS)

  • Customer services analysis (Internal)

Discount driven Customer Analysis

  • Customer segment based on promo influence

  • Offer base customer sales performance.

  • Effective or no effective promotion/discounts.

  • Discount/offer schemes footfall and conversion.

  • Promo margin analysis.

Customer Analysis base on influence

  • Fashion or style change analysis based on movies, social events, etc.

  • Impact on the customer behavior when demand increase.

  • Forecasting based on future events demand.

  • Forecasting based on change in demand due to season, weather, economy, uncer-tain events.

We are happy to help you!

Data is everything, when looking into data everyone have their different prospective related to there skill and understanding but the data speak beyond. Fabi Analytics dig in data to see all kind of information.

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