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Improving Fieldforce effectiveness in grocery
 

Improving Fieldforce effectiveness in grocery

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20:20 RDI has a unique model and method for improving the ROI of fieldforce visits. This presentation talks through the methodology in detail and show you how a simple proof of concept workshop could ...

20:20 RDI has a unique model and method for improving the ROI of fieldforce visits. This presentation talks through the methodology in detail and show you how a simple proof of concept workshop could save your FMCG business millions. Red Ark in Sydney is the local representative for 20:20 RDI who work with all the major FMCG marketers across Europe and the UK. As shown to the Australian Sales & Marketing Institute.

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  • Strongest relationships with PZ Cussons, General Mills, Colgate-Palmolive, Scottish & Newcastle, Danone Waters. Oz projects conducted with PZ Cussons and Diageo.
  • DAVP calculation methods are explained in the appendix slides (23-25) We get the data in two ways - directly from the retailer extranet (we ask the client to download reports whose parameters we specify, then send them on automatically to us) which for Woolworths is called WOW (Woolworths on Web) and Coles has a similar system. Alternatively we can get the data from a 3 rd party used by the client – AC Nielsen or Aztec Synovate. We need the data to be at store level, weekly (daily is even better) and for all the client ’ s skus. If they are category captain or leader they may have access to data for their competitors as well (called category level data) which is even better as we can then calculate the client ’ s market share in every store and compare to national and regional averages to get an even better handle of SOP
  • This is based on real data in Tesco (they have a strategic relationship with Woolworths and are market leaders in the UK) and Asda (linked with Coles and number 3 player in the UK – more of a value, EDLP oriented proposition) in the UK across General Mills products The Size of Prize is the calculated value of resolving the DAL issues highlighted by the model Whilst we don ’ t have a perfect pareto of 80-20, we do see that just over half of the stores represent 80% of the prize – in other words the problems reside disproportionately in a small number of stores. This reinforces the point that much of the field sales effort is ‘ wasted ’ by checking stores which are near enough to perfect compliance to make no difference. There may be a couple of minor problems on slower moving skus but the increase in sales related to resolving these issues is not worth the cost of the call
  • DAVP calculation methods are explained in the appendix slides (23-25) We get the data in two ways - directly from the retailer extranet (we ask the client to download reports whose parameters we specify, then send them on automatically to us) which for Woolworths is called WOW (Woolworths on Web) and Coles has a similar system. Alternatively we can get the data from a 3 rd party used by the client – AC Nielsen or Aztec Synovate. We need the data to be at store level, weekly (daily is even better) and for all the client ’ s skus. If they are category captain or leader they may have access to data for their competitors as well (called category level data) which is even better as we can then calculate the client ’ s market share in every store and compare to national and regional averages to get an even better handle of SOP
  • DAVP calculation methods are explained in the appendix slides (23-25) We get the data in two ways - directly from the retailer extranet (we ask the client to download reports whose parameters we specify, then send them on automatically to us) which for Woolworths is called WOW (Woolworths on Web) and Coles has a similar system. Alternatively we can get the data from a 3 rd party used by the client – AC Nielsen or Aztec Synovate. We need the data to be at store level, weekly (daily is even better) and for all the client ’ s skus. If they are category captain or leader they may have access to data for their competitors as well (called category level data) which is even better as we can then calculate the client ’ s market share in every store and compare to national and regional averages to get an even better handle of SOP
  • A screen shot of the actual targeting tool set up for General Mills. This is the source data for the previous slide. The client gets a password protected area of our extranet and can manipulate the call file by imposing a target ROI for a store visit. This is calculated by expressing the cost of the cost as a ratio against the potential Size of Prize. The cost of the call is calculated by adding together the number of problems identified by a pre-set (agreed with the client) number of minutes required for a fix multiplied by an hourly sales cost. For GM a distribution fix is allowed 20 minutes per brand for example, whilst erecting a missing promotional dispolay on a Gondola End is set at 60 minutes. This extract shows that by imposing an ROI of 15 to 1 (for every dollar we spend on the sales force we want to visit only those stores with at least $15 worth of opportunity). In practise the shortest call will still be around an hour (circa $50) so the SOP threshold will be $750. The pink bars show the prize attributable to Availability gaps, the blue bar is Distribution gaps
  • The only client we ’ ve found who is doing anything like this is Nestle which has the biggest dedicated salesforce in Oz – over 100 full time sales people and 450 part time merchandisers
  • Source – census data with every household categorised into 57 groups Overlaid with shopper research of a sample of 25,000 consumer to get a typical profile of a consumer for the client ’ s product – this example is for Haagen Dazs ice cream A Cameo index of 100 means that the incidence of HD consumers is ‘ even ’ compared top the general population Any Cameo group with an index over 100 has a disproportionate number of HD consumers, and one with less than 100 is under-represented. Unsurprisingly given a price of $10+ per tub the top 2 socio –economic groups have a heavy weighting of HD users With postcode based census data we can then extrapolate the findings to get the geographic hot spots of consumers in the UK for HD
  • We can then use gravity modelling techniques to associate these consumers to specific stores, based on the driving time to each store from their home location This is a map of Blockbuster Video stores for HD in the UK Over-performing stores are a source of Best Practice for the client – what are we doing right here that we could replicate in other stores? Under-performing stores have plenty of consumers (the Y axis) visiting the store but ehy are not converting this into sales (the X axis)
  • Trade marketing resources prioritised according to new way of segmenting stores by potential. Used by General Mills to select 150 of the 650 Blockbuster stores for a new display freezer
  • We ’ ve been suggesting a cost of $10k for this We need 2 weeks to turn the data into analysis for a workshop If they decide to buy the full service from us (minimum $55k) we ’ ll give them a year one discount to the value of the workshop!

Improving Fieldforce effectiveness in grocery Improving Fieldforce effectiveness in grocery Presentation Transcript

  • 3 out of every 4 store visits areeffectively a waste of time and moneyHow you can cut costs & dramatically improve yourfield marketing ROI today and tomorrow
  • Why I am hereTo help you• Focus your field marketing investment to those stores / outlets / branches with the greatestpotential to improve your ROI by:• Generating up to 20% increased sales revenue during periods of promotional activity• Giving you better insights into your customer or your channel• Enabling you to implement better through the line activity and in-store activations• Improving the performance of your field sales / merchandising teams
  • • Diageo– UDV– Guinness• Charles Wells• Scottish Courage• Interbrew• Seagram• Matthew Clark• PepsiCo Drinks• Constellation Wines• Weetabix• Danone Waters• Ferrero Rocher• KronenbourgHow I know what we are telling you• Twinings• Nabisco• Nestle• CPC• Sara Lee• Frito-Lay• Hershey• Golden Wonder• Aunt Bessies• Campbells• Leaf Confectionery• Warner Lambert -confectionery• WalkersSnackfoods• Masterfoods• General Mills• Centura Foods• Colgate-Palmolive• Levi’s• Gillette• PZ Cussons• Procter & Gamble• Spalding SportingGoods• Elizabeth Arden• Reckitt Benckiser• Imperial Tobacco• Gallaher plc• Philip Morris• SCA Hygiene• Dunlop Slazenger• Interflora• CoMag• Buena Vista• Frontline• McCain
  • • Distribution, availability, visibility and promotion are all critical to gettingthe product in the shopping trolley• Leaving in-store performance up to the retailer and your field marketingteam is tempting – it’s hard, it’s complex, and how can you calculate thereturn for your effort anyway?• The easiest and “safest” solution for most brand owners is to direct theirresources to the stores with the most “risk”, those ones with the highestsales• This kind of thinking is expensive………….. whether it’s because yourin-store activity isn’t being properly implemented, or because it is• Up to 75% of stores are compliant when visited, meaning 3 out ofevery 4 field force visits are a waste of time and money
  • Typically 80% of sales potential is in only 55% of stores –so why call on those that don’t justify it?
  • This is what we found from a recent effectiveness audit for a leading brokers salesforce supporting a major FMCG brand stocked in Woolworths and Coles with apotential sales value (size of prize) of $24 million per annum▼ 14% of stores received fewer calls than was agreed with the brand owner▲ 22% of stores were called on more often than agreed (sometimes much more often!) with nodiscernible sales benefit■ Over 3 months, 300 stores received 2 visits in one day – according to the visit data. Why?•There was no correlation between number of visits made and sales volumes achieved. Ofstores with 10 or more visits over an 8 week period, the top had an average weekly sales valueof $71,000, the bottom an average sales value of $8,000•In Woolworths 80% of the additional opportunity (size of prize) was in just 42% of the storesvisited. In Coles 80% of the size of prize was in just 38% of stores visited•Effect of sales visits on promotional sales in one Woolworths promotion:- Group of stores receiving 7 or more visits achieve a 154% uplift- Control group of stores not visited at all achieves a 180% uplift!•In 278 Coles stores, we found that the product had not been scanned at any time in the 13week launch period.•But visited Woolworths stores performed better than our non-visited control group – they startedscanning the new product several weeks earlier on average
  • It’s a big problem, but it can be fixed• Most brand owners have no measure of the ROI on the Field Marketing investment.• Promotional uplift varies for a variety of reasons– Distribution & availability issues inhibit sales!– Nature of the promotional offer itself– Mix of products & their responsiveness– Compliance - dates, products, displays– Propensity of the shoppers to respond to the offer, according to socio-demographic mix of the catchment consumer• Intelligent analysis of store level EPoS data combined with field marketing informationabout their in call actions can highlight the gaps and direct resources only to thosestores requiring a fix, cutting coverage costs and dramatically improving the ROI ofyour field marketing effort
  • How we analyse and drive ROI• Compliance gaps reviewed against classic sales drivers (DAVP)• All stores in the retailer estate included• Each gap is identified at a sku level, valued in the context of its potential contribution (lost sales) &modified according to the total value of your sales in that outlet• DAV gaps are assessed based on most recent data available• Promotional gaps forecasted using predictive modelling tools based on historic performance of yourbrands in each specific outlet – highlights late starters, early finishers and ‘basket cases’!• Stores segmented according to total potential to increase sales• Resources are then allocated or reduced/removed accordingly
  • Imposing a threshold 15 to 1 ROI on a database of 1600 storesprovides a call file of 883 stores (55%) but retains 80% of thepotential sales increase
  • Improving ROI at store level
  • Ranking by percentage uplift addressesvariations due to store size, but still….???Drilling down
  • Late start, early finish and lack of availability halfway through= poor performance
  • Example 2, high uplifts, but inconsistent
  • Was the product in the store?……
  • Was the product in the store?…..
  • Reasons for variations
  • Past performance for future returns
  • Prevention vs cure
  • Action plan depending on compliance
  • For example, 3 visit stores have biggest potentialto improve here
  • Beyond in-store compliance…• Many other reasons for store level under-performance– Low consumer demand / too few target consumersin the catchment area of the store– High concentration of other grocery outlets in theimmediate vicinity of the store– Poor category position in this specific store – lowerthan expected shopper traffic– Other local or store specific reasons• The start point is to segment the stores according toyour own assessment of consumer demand for yourbrands**20:20rdi works with GMAP as its exclusive partner for socio-demographic profiling
  • Start point– analyse areas of strong potential consumer demand andallocate those consumers to small post code sectors• We start by mapping consumer demandby postal sector using– TGI (Nielsen) survey data for yourbrands– FES data– Census data– Cameo Socio-demographic profilingspecific to your brands• Highlights groups of consumers morelikely to be using your brands• We map them geographically in 57Cameo groups or types (top linesummary of 10 super groups shownbelow)
  • We then use powerful gravity modelling tools to allocateyour target consumers to the retailer’s stores• Store catchment areas definedby drive times, consumerprofile & proximity ofcompetitor outlets• Forecasts total # of consumerslikely to visit each store• Also predicts how many ofthose will be your customers -% of profile consumers in aparticular store• Highlights underperformingstores which will receivepriority action• And also over-performingstores which represent ‘BestPractice’Example – Luxury Ice Cream in Multiple Convenience store group
  • Insight enables targeted deployment of a range of marketing tools –and also some decisions about where not to investLocal PostersExperientialeventsCouponsSamplingNew POPunitsStaff training& incentivesNo POPrenewalStore Estate map
  • • Combining EPoS data, promotional history &consumer demand information we can build storeclassifications that enable the brand owner to selectthe appropriate customer activation tool to drivepenetration, sales & category share• And the same data sets allow us to track, measureand refine the approach as we see the resultscome through• The benefits are compelling – in simple terms wecan reduce trade spend and increase sales!
  • Want to know more?
  • Easy Next Step - look for quick wins• 20:20 RDI runs a proof of concept workshop where we take 12months of EPoS data, review the impact of major promotionalactivity, run the model and demonstrate the missed potential• The objectives of the workshop are:– To analyse the impact of 3 recent promotions within a single retailer– To look for differences and similarities in executional compliance atindividual store level across the 3 events– To examine the opportunities offered by the differing sales driverswithin the retailer(DAVP)– To calculate a potential Size of Prize (SOP) associated with the retailerright now– To stimulate cross functional debate around the findings– To create hypotheses around the causes of the findings– To demonstrate the benefits associated with mining store level EPOS• The deliverables will be:– Analysis of 3 promotions– ‘Rogue Store’ analysis– ‘Best Practice Stores’ review– Promotional compliance Size of Prize– Distribution gap Size of Prize– A cross functional team motivated to challenge the way they currentlydo business
  • What data do we need to run a workshop?
  • Simple really…………
  • For more info on 20:20 RDI or to arrangea proof of concept workshop, contactLiz Rowell at Red Ark.+612 9437 1377liz@redark.com.auwww.redark.com.au