Sales data analytics of retail orders taken online on mobiles

Analytics opportunities with sales order data collected online using mobile devices

Presented By: Voiceback Analytics, BangaloreApril, 2020Retail sales process automation includes digitizing the entire retail chaindistributor to retailer, digitizing the sales force visits and order taking. There areapproximately 40 lac small retail outlets across India, the grocers, theconvenience stores, the mom & pop stores which do not have any computerizedinventory system, billing system and therefore any ordering system yet. In ruraland small towns of India, almost all outlets are still on manual inventory keeping,billing and ordering. And even in larger towns, no more than 5% outlets have anyform of mechanized inventory keeping, billing and ordering.

So has the sales process automation skipped these by non-computerized outlets?Not really. The process may not yet be total end to end automation for theseoutlet but when the salesperson visits these outlets today, he uses a platformon his smart phone, logs the visit and enters the retailer’s order or no order!This connects the buyer (retailer) to the seller (distributor) in real time and cutsthe time that data transfer would have taken in paper era. It can also preventany error in order taking, it can convey information of any stockouts etc.instantly and the buyer (retailer) can work out other solutions etc.

Many corporates in India and worldwide have moved from taking retail orders onorder form prints to online apps on the mobile. And many many others are stillevolving on developing the systems for collecting orders from retailers online onmobiles.

Taking orders online using mobile devices has direct benefits which impact salespositively. This process gives sales reps the opportunity to sell more. It increasesefficiency of sales reps by them taking orders on the app instead of manuallywriting them down on paper. It eliminates outdated price lists & stock levels. Itkeeps Sales Reps updated with latest products, prices, promotions and updatedstock levels. However, we are not dwelling on these direct advantages in thisdocument. This document proposes to dwell upon how data analyticson this data collected for taking orders can be used for enhancingbusiness efficiency at multiple levels.

1Sales analytics of retail orders data to enhance business efficiencies

Sales data analytics of retail orders taken online on mobiles

New OrderPayment history

Dispatch status

Product cataloguePromotions

Retailer IDSales Assist app from VBT does the following• Showcase your Product Catalogue with multiple product

categories & multi-level category nesting, with photos• Take orders from any business partner – retailer,

wholesaler, distributor etc à Eliminate manual entry oforder taking

• Have updated payment history• Have updated price lists online, update it with any

specific promotion offers on a daily or even hourly basis• Track the route followed by sales rep, calls made by the

sales rep• Digitize your entire retailer network

Using sales data analytics to enhance business efficiencies

2Sales analytics of retail orders data to enhance business efficiencies

Using retail order taking apps, Sales Reps, Distributors, Wholesalers, Dealers &Retailers can Browse the Catalogue, place orders, update payment, check dispatchstatus and check past history of retailers at any of these levels.

Apart from making the sales & distribution process seamless even for millions ofnon-computerized outlets, this automation process generates vast amount ofother real time data, which is a byproduct of the process. Sales processautomation has thus brought in about two great changes to the entire system.

1

With no regular dedicated data entryeffort, all data regarding purchasehistory for each retailer is readilyaccessible. This makes all this dataavailable for churn to understand thespecific outlets with pre writtenalgorithms. Analytics of this datahelps in enhancing salesefficiencies by givingopportunities for cross sellingand upselling.

This system tracks the exact itinerary thatthe sales person follows from the start ofhis day till the end - the geography ofwhere he goes, time he spends at eachstop, records co-ordinates of each outlethe visits. Analytics of this data helpsin evaluating productivity ofdistribution chain to drop orimprove unproductive legs of thechain and save distribution costssignificantly.

2Enhance sales efficiency

Enhance process efficiency

Sales data analytics of retail orders taken online on mobiles

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3Sales analytics of retail orders data to enhance business efficiencies

1 Using sales order analytics for enhancing sales efficiency (1/2)

Mobile based order taking systems are comprehensive and ideally need to besynchronized with information listed herewith for ensuring a holistic impact onsales revenues. The data it should synchronize with is:• Payments receipt dates and records of pending payments• Dispatch status• Complete product catalogue with updated prices – should be linked to master

price list• Updated inventory level• Updated promotion details for each product• Qualitative inputs like any push required for any specific SKU (eg., the SKU is

under promotion currently)• Details of all past Orders taken on the system

This system helps enhance sales efficiencies at two levels:

These are detailed herewith in the next section. Additionally, this system can alsobe used for developing a retailer level Credit Management Program. This is out ofpurview of this document as this focusses on sales efficiencies enhancement.

1a) Analyze order data at different levels

1b) Recommend Retailer Sales plan to the sales person

1a) Analyze order data at different levels

Trend analysis is done on the order data at different levels –• Retail outlet• Sales person• Sales Beat / zone• State / zone etc

Correlations of trends of order value at these will done with various otherparameters in the system as the ones listed above. This will help us answer howaspects like price changes, promotions, inventory level, stock outs etc. is impactingorder value.

Contd …

Sales data analytics of retail orders taken online on mobiles

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4Sales analytics of retail orders data to enhance business efficiencies

1 Using sales order analytics for enhancing sales efficiency (2/2)

1a) Analyze order data at different levels (Contd)

This analysis will help answer questions like …• Price changes are enhancing order value for which SKUs• Promotion offers are delivery higher returns for which SKUs• Geography feedback à beats / distributors / states etc are responding

well to specific types of offers and or price changes• Retailers feedback à identifying retailers who respond better to

promotion offers vs. those who are always consistent etcThis analysis will help developing a micro sales strategy targeting such that wecan optimize order width and depth across SKUs and retailers.

1b) Recommend Retailer Sales plan to the sales personSales strategy has in the past been limited to targeting retailers for promotionslike QPS, dissecting the retailer base using ABC classification. Key deliverable ofmobile based ordering system is that unlike ABC system of classification, itbelieves in customized Retailer Sales Plan for each retailer.• Sales person can access history of past purchases of the retailer and see

products / SKUs ordered by him in the past. And accordingly make a salespitch trying to widen the basket base and enhancing the depth of favoriteSKU picks

• More efficiently, the system has preset algorithms derived from the analysisof all past data and recommends order guidelines for the specific retailoutlet. The rules in the algorithm are set in accordance with sales strategyderived at stage 1 above and could involve aspects like• If retailer picks SKU A 3 units, push 4• If retailer picks SKU A 3 units, offer 5 + 1 (free)• If retailer picks SKU A, B, C, definitely offer D, E• Include products which are under promotion• Include products for any other strategic reasons (packaging change is

likely and you want to remove old packaging stock)

Thus the system pushes the order value to go up in such a mannerthat is beneficial both to the retailer and the company.

Sales data analytics of retail orders taken online on mobiles

5Sales analytics of retail orders data to enhance business efficiencies

One of the main targets of a sales manager is to identify productive calls,productive people and understand patterns which impact productivityof the entire chain, ranging from sales beat to individual sales person,down to each of the retail outlets being covered. However, the traditionalsales processes would not give the sales manager the luxury of confidence to takethese decisions.

The traditional systems of recording sales visits and orders could easily captureinformation like• Calls made in a day, in a month etc for each sales person• No. of productive calls made in a day, in a month etc for each sales person• Productivity of specific outlets• Revenue generated from each outlet

Some systems could be more rigorous and attempt to capture some moreinformation from each salesperson like types of retailers covered in a day, distanceand time spent on field etc, which might impact productivity. Validity of this datawould always be moderate at best. Therefore, for lack of some data and lessreliability of some data, lack of timely availability of some data, the system wouldnot really be used to conclusively assess productivity of people and routes.

Data captured by order taking on mobile ordering systems will precisely assess

• Distance and time taken from one outlet to another• Time spent by the salesperson per outlet• Productivity patterns for outlets ie., understanding in how many days is an

outlet becoming productive (as any interim visit is likely to be unproductiveand therefore redundant)

• Productivity patterns for sales beats and even sales persons

Infact the moment a salesperson deviates from the fixed path, the managersphone could ping !

2 Using data captured in the order taking process to enhance process efficiency (1/2)

Sales data analytics of retail orders taken online on mobiles

6Sales analytics of retail orders data to enhance business efficiencies

And voila ! Now some churn of this data and we can answer questions like:

Therefore, earlier one could never be sure if a salesperson or a retail outletidentified as unproductive is so for lack of capability and / or effort? Or it he isidentified as unproductive for our lack of understanding these essential butimmeasurable factors. With mobile based order taking systems, we canevaluate the process and people efficiencies precisely and get concreteguidelines for enhancing process of the less productive people, beats,processes etc.

2 Using data captured in the order taking process to enhance process efficiency (2/2)

• Are the less productive guys reallymaking all the calls they claim? Are theyspending as much time per outlet asexpected? Or more or less?

• Are the guys making more visits, moreproductive or guys making fewer visits,more productive?

• Is there a correlation between timespent in an outlet and revenuegenerated from it?

• Are some beats really designedinefficiently because of distance andtraffic conditions?

• What is the cost and revenue trade-offfor outlets at the bottom of thepyramid? Can we drop some outletsand make the system more profitableby reducing costs?

• Can we identify the outlets we shoulddrop to enhance profitability of thesystem?

• Can we identify entire sales beat/s weshould drop to enhance profitability ofthe system?

• REWARD THE PRODUCTIVE GUYS !

• GUIDE THE LESS PRODUCTIVE ONES WITH CONCRETE INFORMATION !!!