Assignment 6 – Data Value Templates

Develop an innovative data initiative that draws on using data more effectively using one of the
data value templates you learned about in this module.

TASK

Choose a business unit or division of your own company, one that you are familiar with or
involved in, and then answer following questions.

1. What is the division or business unit of your company that you will focus on?

The business I am considering for this assignment is a CPG business (India) The brand is the
No.1Skincare brand in India. The division I am considering is Sales. More specifically front-end
sales automation.
Context: Front end sales automation has been on a growth spurt in India. Almost all CPG
companies have adapted cloud-based technology enabling their sales force to place and process
order across all the retail outlets. We currently cover around 1mn retail outlets (In India, large CPG
business would cover 3mn outlets directly through large sales force organisation). Traditional
mom and pop stores (retail outlets) contribute to ~ 70% of business (these stores have gained back
their prominence and saliency in Post Covid era). Convenience, proximity to consumers,
relationship-based engagement with shoppers, availability of assortment and home delivery
options have ensured their continued prominence. Each of these outlets are serviced by sales
persons directly on a weekly or bi-weekly basis. Front end sales are strong on relationship building
and soft selling skills but most sales calls or product orders are based on the past experience of the
sales person, his biased opinions, his priorities (incentive maximisation) or past experiences.
Current focus of the automation has been around increasing efficiency and digitising a routine
process. But the data captured gives us real time access to current trends, offtake overview, and
insights into shopper behaviour. Machine learning, AI and predictive analytics can catapult sales
growth by identifying opportunities, upselling, assortment and pricing strategies enabling sales
person to focus on retailer relationship building.

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 2. Which template for data value creation will you use? As a reminder, the templates are
Insight (revealing the invisible), Targeting (narrowing the field), Personalization (tailoring
to fit), and Context (providing a frame).

The template for data value creation will be Insights, the data collected through all orders across
different categories and segments can reveal some valuable insights around the assortment, pricing,
consumer preference in the area, potential new product opportunities that will enable the business
to deliver incremental business growth apart from improved efficiency. It will arm the sales guy to
focus on relationship building and allow data to guide him in terms of all other parameters.
While, other templates like personalisation, targeting and context will be put to use to create
business impact and link back to customer life time value

3. Brainstorm – how will you create new value for your business using this template?

Reveal assortment opportunities to improve efficiency: Currently salesman had very limited time per outlet
and has around 100+ SKUs.ML and AI can suggest the right assortment to maximise sales in the outlet. We can
combine historical data, returns, promotions, similar store behaviour. This will have business impact (6-10%)
by increasing effectiveness in the limited time.

Retailer preference, behaviour, shopper/consumer profile to deploy personalised retailer program to

maximise ROI on promotions and apt pricing strategy: Pareto principles applies in all business, 20% of the
retailers would contribute to 80% of the business. This will enable us to identify and deploy curated programs
and promotions for our top retailers for our business and for individual product lines. Result would be
optimum deployment of resources and high ROI by selling more and better where you can.

Reveal insights to determine best Sales route optimisation: combining maps data along with retail stores
performance, the data can recommend optimum sales route based on cost and sales maximisation.

Retailer shopper basket analysis to pinpoint New product opportunities Data and analytics can enable the
optimum GTM to the new product depending on the past performances, retailer behaviour and footfalls,
geographical location and consumer behaviour in the location. Early offtake measure, retailer feedback can
also give us feedback and can modify the pricing/promo/communication, assortment, variant performance
more effectively to ensure success of the NPDs.

Visual merchandising and PoS impact: The data could help us track real time impact of various claims and
offer communication and POS elements. Data could also enable shelf assortment mix and visibility impact, Ex:
should the skincare creams be visible on basis of benefits nourishment, anti-aging, acne, tone correction or
should they be placed on basis on consumer life stage, for teens, for kids, for young adults etc.

Insights on retailer motivation/behaviour to develop Better objection handling: Since this is integrated
system, we can share different objection handling techniques to queries based on experiences in other parts
of the country.

Insights to determine source of organic (new assortment or range opportunities in existing customers and
inorganic growth (new or dead outlet activations)
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 4. What data will you need to make this initiative work? For example, would you need data
about your customers’ behaviors or their preferences or social ties? Would you need to
get data from third parties?

• Customer Relationship Management (CRM)

• Integrated daily sales order (geography wise, sales office wise)
• PoS data from the retailer’s consumer billing and inward (product purchase) data
• Inventory data at the retailer
• Need third party data like MAPS, syndicated offtake data

5. How will you get this data? How will you convince your customers or business partners
to give it to you? Customers may be more willing to share data with a company they
trust if they see relevance or value to them.

While most of the data will be collected through the Sales force automation system (real time sales
order management), the critical element would be to collect and integrate retailer PoS data which
will reveal insights on shopper behaviour and inventory velocity across various categories at the
retailer. Retailer or customers may not be willing to share this data with the company. Traditional
mom and pop stores in India are small time family run business. Many of them don’t have
integrated inventory management and PoS based systems. The retailers have challenges in cash
management, tracking of offtakes and tertiary orders and real time inventory management.
A centralised and integrated PoS system would be a relevant investment for the retailer. This is
simplifying their process of order and inventory management. They will be able to stock assortment
on basis of velocity and consumer preference in the store. The retailer will also be able to
recommend additions to the consumer basket based on shopper bill analysis. What the some of the
other products consumers with similar profile are buying etc. The data will also enable them to
build a loyal base of consumers and offer personalised services like home delivery, subscription and
loyalty programs.

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 6. What will be the benefit to your business? What is the measurable outcome – what do
you think will run better or be more profitable or more effective if you make this data
initiative work?

The most important impact is understanding the consumer and shopper purchase behavior. Application of
behavioral and data science would bring to light new insights through consumer basket analysis(POS data) Ex
: What are the other complimentary products that are included whenever she buys a cream, impact of
weather on the variant (Ex Does she purchase a mint or cucumber based products more in Summer and fruit
based variants in Winter).these insights can develop relevant new products and assortment.

According to one of the most recent google model on shopper behavior, the trigger to preferring to one of
their brands in the consideration set is just to be available (JUST SHOW UP). The front-end sales force tool and
Machine learning will recommend optimum assortment that will maximize sales in the outlet. This will take
into consideration type of the retailer, shopper profile based on the location, historic velocity of the
recommended line based of other similar type of outlets, surrogate brand velocity, out of stock % in the
current store etc. We can also deploy the right visual assortment mix based on benefits or price range or age
profile
Improve productive calls: Combining MAP data with the retail coverage plan, the tool can recommend the
optimum sales route for the day. Combine this data with the type of outlet, outlet credit score, we can
recommend the time to be spent in the outlet, the must visit retailers to maximize productive and value sales
for the day. By identifying dead outlets ( not billed), their past performance we can reactivate the same by
understanding key barrier and recommending appropriate solution

New product deployment: Key success factors for new product deployment is the early adopter accessibility.
The right place
Sources: availability is critical. In this case, we can overlap bull’s eye TG demographic and location
https://www.indianretailer.com/article/technology/in-store/Next-stage-of-SFA-through-
profiling with surrogate or competitor brand’s saliency & performance in the outlets to identify must win. We
Machine-Learning-Artificial-Intelligence-in-FMCG-retail.a6370/
can also determine the optimum support model in store
Ex: Visual merchandising, best pricing, combo offers, free sampling or retailer recommendation to maximize
trials for the new product line. Repurchase rate, enquiries could also be early indicators of performance of the
new product.

Trade spends in organization needs detailed tracking. With limited resources, it is important to identify
optimum utilization. The big data can recommend the optimum spend distribution to maximize ROI on basis
marketing input plans, past performance, type of outlet, consumer sentiment.

Effective visibility and Point of sale elements and communication:

The AI based algorithm can recommend different communication to be deployed based on the consumer and
shopper profile: For ex: Best deals during pantry loading days (1st to 10th of every month), new product
features and performance (during weekends when the luxury women shopper is browsing through the
assortment). This can also equip our promoters to recommend the appropriate upselling basket.

Unlocking growth through loyalty programs and subscription

Some tangible KPIs are

Improve efficiency: Productive calls, time per call, number of lines sold per outlet, NPD coverage
Improved effectiveness: Reduction in dead outlets (not billed for 3months or so), range availability, right
assortment mix, optimum pricing, effective visibility, trade spends reduction.
Improved effectiveness: Reduction in dead outlets ( not billed for 3months or so), range availability, right
assortment mix, optimum pricing, effective visibility, trade spends reduction,
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