SOLUTIONS

  • What products have customers liked ?
     

  • Time when customers are most likely to purchase ? Hourly - Day of month - Month of year
     

  • Preferred Price range 
     

  • Popular Style of product 
     

  • From where customers are viewing / purchasing ? (Highly localized analysis) 

     

How to attract new consumers

01

With Dynamic Behavior Mapping and identifying different consumer cohorts predict consumer interest patterns/how and when to attract new consumers.

The focus here needs to be firmly on customers who have visited but not purchased. We offer wholistic solution to analysis / predict multitudes of exit points. 

 

These could be 1. first time or repeat customers OR 2. registered or un-registered customers. 

 

Exit-Points could be ,

 

  • Product Page 
  • ​Non- product Page 

  • ​Cart exits 

  • ​Returns

  • ​Shop Floor analysis 

02

How to attract new consumers

Understand why some consumers visit the product pages but it does not convert into a buy.

We consider repeat to be of 2 types,
 

  • Purchased multiple products as first time customers (first purchase cycle).
  • Purchased more than one product over period of time.

Based on individual customers historical data we can predict,

  • Most likely products to be purchased. 
  • Optimum time purchase can occur.

  • Fine tune individual customer communication. ​

03

WHO SHOULD WE TARGET AND WHEN?

Insight into Active and Passive consumers and the ideal time and products to be recommended to them.

Comprehensive report of Footprints which captures the product journey of each and every product within the system along with its categories. All product-based data will be supplemented with time and geo location ( when available ) of the user interactions with that particular product.

Following are the list of reports that will be generated,

  • GAIN AND LOSS ANALYSIS

  • TIME ANALYSIS

  • GEO – LOCATION ANALYSIS

  • PRODUCT OVERVIEW

  • PRODUCT FOOTFALL MAPPING 

  • CATEGORY FOOTFALL MAPPING 

04

WHAT IS THE ONLINE SHOPPER JOURNEY?

Gain Loss Analysis, Time analysis, geo-location based shopper product analysis and footfall mapping gives in-depth insights into shopper journey.

  • Learn from Past campaigns 
    Based on past data related to previous campaigns and their performance, we can now forecast the possible overall  impact of the new campaign. 

  • Learn from Competitors campaigns 
    If there is data of competing entity then we can also extract the knowledge from their performance and use it to improve our predictions.

  • Give absolute best time to for campaign execution
    A. how particular brand and their competitor have performed, 
    B. when a set of  competitors has run  there campaigns

05

HOW DO WE GET MOST OUT OF CAMPAIGNS

Analyze past marketing campaigns and give actionable insights into future campaign planning

  • Analysis to help inventory decision for shops
    Based on the prediction and forecasting of Online consumer behavior with respect to specific regions, we will provide analysis that could help inventory decisions for the retail outlets.

  • Learn from Competitors campaigns 
    The previous product similarity mapping will also aid in planning sales executive strategies at the shop floor of retail outlets. Example, 

    • Planogram of products

    • If consumer likes a particular product, which other products should the sales executive recommend to him

    • Based on the demand, what are the likely inventory needs for that region.

06

WHAT CAN WE LEARN FROM SHOP FLOOR

Base your inventory management decisions and product planning and placement on analytical insights