Mobile BI, Analytics Tied to ERP Informs Faster Promotions for Field Sales Reps at Hillarys Blinds

by   |   April 1, 2013 6:37 pm   |   0 Comments

Hillarys Blinds, a store-less retailer which makes products to order, deployed a mobile BI system to adjust promotions quickly based on competitors’ offers and data from sales managers in the field.

Hillarys Blinds, a store-less retailer which makes products to order, deployed a mobile BI system to adjust promotions quickly based on competitors’ offers and data from sales managers in the field.

Fancy a new window blind?  Or an awning, or a set of curtains?  Hillarys Blinds, Britain’s largest made-to-measure blinds company, will send a self-employed sales advisor to consumers’ homes, ready to help them choose colors and fabrics, measure-up their requirements, and take an order.

At which point, the advisor opens up his or her mobile device—a Samsung Galaxy SII, running Android—and enters the order, live, into Hillarys’ SAP ERP system, taking payment at the same time.

“Before we went mobile, we used to have an army of people here in Nottingham opening envelopes and keying orders in—entering them directly reduces the cycle time, cuts the error rate, and delivers a faster order-to-cash process,” says Julian Bond, head of information technology at Hillarys, which is based in Nottingham and manufactures 30,000 blinds or curtains a week, each sold direct to customers through its network of 950 advisors.

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But the real gains, he relates, lie elsewhere.  Thanks to business intelligence from SAP BusinessObjects, and mobile analytics developed for the company by consulting firm AgilityWorks, Hillarys’ management team can react more quickly to changes in the market.

“The retail market changes from week to week, and we need to be responsive to those changes,” says Bond.  “Adjusting our advertising, adjusting our promotions and pricing, and countering our competitors’ tactics.”

Better still, that agility extends to the regionally-based field sales managers at Hillarys, each responsible for managing 25 or so advisors spread across a handful of counties.

Equipped with the same devices as their advisors, but with software targeted on delivering dashboarding and analytics functionality rather than order-taking, the field sales managers have a near real-time view of the marketplace—a view that’s as up-to-date as the latest sale in a consumer’s living room.

“When we began the roll-out last summer, field sales managers were ecstatic,” says Bond.  “They’d say to us: “We can do things today, while we’re out on the road, rather than when we get back to the office next Monday.  We can respond to what’s happening today, make a change, and see the benefit sooner.”

But although the deployment of mobile analytics is just months old, the ground work lies further back, in a decision in 2008 to implement a business intelligence solution alongside the company’s SAP ERP system.

The driver: a realization that in such a promotion-led organization, the weekly trading meeting to review the last week’s performance—which took place every Tuesday afternoon—was too late for changing marketplace conditions.   Also troubling, adds Bond, was the amount to time being taken to develop ad-hoc analyses in response to management requests for more information.

“The accountants were saying: We need something better than Excel,” he sums up.  “The more we looked, the more we realized that business intelligence was the way forward.”

Hillarys liked IBM’s Cognos, Bond says, but SAP’s BusinessObjects meshed better with the product variant configuration tool that was built into the ERP system—an important requirement in a business such as Hillarys. “We have 4 million size permutations in half a million combinations of fabric, color, options and accessories,” explains Bond.  “Multiply the two numbers together, and you begin to see the size of the problem.”

With SAP BusinessObjects implemented, it was possible to bring the Tuesday meeting forward to 2 p.m. on a Monday—and for that meeting to be better informed, with the preparation of a large part of the weekly review pack now automated. “As a business, we’re very promotionally-driven, and you haven’t got long to influence the next week’s numbers,” says Bond.  “Meeting on a Monday instead of a Tuesday makes a big difference.”

Mobile BI Rollout  
Roll the clock forward to 2012, and a decision had been made to replace the company’s first-generation Windows Mobile devices, dating from 2005, with Android devices.  Bond says Hillarys saw a problematic upgrade path for Windows Mobile, and an enhanced ability in Android devices to securely take payment while in customer’s homes.

Tasked with delivering the change was British IT consulting firm AgilityWorks, charged by Bond with starting fresh. “We didn’t want to just port over the old application to the new devices,” he explains.  “Starting from scratch enabled us to make full use of a modern Android device, together with its capacity for advances such as better product visualization.”

But with the mobile devices successfully deployed in the field, AgilityWorks came to Bond with an intriguing proposition: mobile analytics, with a proof-of-concept to be delivered in 15 days.

“AgilityWorks was already working with us as our partners in business intelligence, as well as the mobile rollout, and they spotted an opportunity to link the two,” says Bond.  “The hard work had already been done in the shape of the data warehouse and business intelligence.  So the data was already there — it was just a question of making use of it by delivering it to field sales managers’ mobile devices.”

And the technology to do that already existed — hence the brief 15 days required to deliver a working proof-of-concept, a timescale which Bond concedes is “pretty much unheard of.”  The shopping list: SAP Crystal Dashboard, a Crystal Software technology SAP acquired when it bought Business Objects in 2007; and SAP Afaria, an enterprise mobile deployment tool stemming from the ERP company’s acquisition of database and mobile deployment specialist Sybase in 2010.

Hillarys subsequently rolled out six top-level metrics. The field sales managers see each metric, together with a green upwards-pointing arrow if performance has improved since the previous week, and a red downwards-pointing arrow if it hasn’t.

While declining to cite many specifics, Bond says there’s a measure of conversion, for instance, citing the percentage of leads converted into orders.  And a measure of average order value.  There’s a quality measure, too, targeted on tracking sales advisors measurement errors, and also a measure of the lead time to resolve customer service issues. There is also a measure designed to illuminate the company’s return on its advertising, and another measure that Bond describes only as “a lead time metric”.

But  of the ROI of Hillarys’ investment in business intelligence and mobile analytics, Bond is in no doubt.  Strategically, for instance, business intelligence enables decision-makers at HQ to respond more quickly to changes in the marketplace.

“Week by week, customer sentiment changes,” says Bond.  “And as metrics such as cost-per-lead and conversion factors change, then we can decide—on the fly—how we approach advertising expenditure nationally, regionally, and locally.  Remember: we don’t have any stores, and we’re dealing with it all centrally.”

And tactically, too, mobile analytics has given field sales managers a real-time view of those marketplace changes.  If an advisor’s sales suddenly drop, for instance, the manager can establish the cause right away—and might discover, for instance, that a local competitor is running a sale.

“There and then, [the advisor] can decide whether to authorize an additional level of discount, or increase advertising spend in the local newspaper,” Bond says, adding: “It’s real-time results, and not a rear-view mirror explaining what happened when it’s too late to do anything about it.”

Malcolm Wheatley, a freelance writer and contributing editor at Data Informed, is old enough to remember analyzing punched card datasets in batch mode, using SPSS on mainframes. He lives in Devon, England, and can be reached at

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