RIS November tech solutions guide analytics
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Through precise location analytics, retailers now can monitor the entire path to purchase. With this data, marketers better understand what led to the purchase providing the ability to move beyond the traditional blanketed “campaign” to a year-round interaction based on consumer behavior. Customers “opt-in” by mobile app to receive highly-targeted promotions, information about merchandise they may have “visited” but didn’t purchase, and discounts for major events – based on correlations like visits, dwell and intent – to drive sales like never before.
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RIS November tech solutions guide analytics
- 1. SOLUTIONSGUIDE technology SmarterInsights,SmarterStrategies There is little doubt that customer analytics, along with the volumes of data that drive it, is empowering retailers with new insights and smarter strategies. Ben- efits are derived by enabling retailers to become more predictive and personal- ized in the way they interact with shoppers and merchandise products. Shoppers get a return for being included in the data and retailers get a return by being better able to manage inventory across physical stores and digital channels. But with that power comes a host of challenges: respecting consumer’s privacy and safeguarding their data; ensuring data accuracy and integration; managing and making sense of the deluge of new data points; and entrusting the data to cloud-based vendors or other third-party providers. Nonetheless, many retailers are now rushing to see how they can best exploit the growing mountain of data and become truly analytics-based enterprises. Properly aggregated, the data can be processed and answers can be drawn from multi-dimensional queries that can segment customers and help deliver highly personalized messaging and mer- chandising programs. Q What are best-prac- tice techniques for segmenting custom- ers and then market- ing to those segments? Kellie Peterson: There are many shop- per segments, but retail traffic analytics, for the first time allows us to segment custom- ers by their in-store behavior, and most importantly, by their intent. Both passive (anonymous) and active (mobile app) lo- cation tools provide insights into shopper behavior and influence how marketing programs are designed to address shoppers by in-store behavior groups. We can seg- ment shopper paths for example, and ex- pertly employ contiguous marketing tech- niques to group products based on where that shopper is likely to go. Retailers can identify specific traffic segments such as repeat visitors, employees, cross-store shop- pers or shopper groups who don’t make a purchase. For non-converting shoppers, we can identify where and when this is likely to occur – and install resources to increase conversion. iInside’s passive technology is anonymous so no personal data is collected or accessible without consumer consent. Bruce Armstrong: At a recent mar- keting conference Gary King, former EVP and CIO at Chico’s, led a great discussion about marketing to customer segments with Kaitlin Moughty from Freshpair. com, Rob Bowers from Total Hockey, Peter Leech (The Partnering Group and former CMO of OnlineShoes.com) and Shelley Nandkeolyar (The Ivory Company and Board Member Emeritus of Shop.org). Bruce Armstrong President & CEO PivotLInk Kellie Peterson Director of Marketing iInside PRODUCEDBY SPONSOREDBY marketing intelligence
- 2. Their conversation highlighted the evolu- tion we see taking place as retailers prog- ress through five stages of marketing matu- rity. They underscored the importance of centralizing customer data from internal systems and third party data sources – something we do so marketers or their IT teams no longer have to do it themselves. Once customer data is unified, retailers can explore who their customers are and what they do, and apply advanced customer analytics to pinpoint the likelihood they’ll take the next action and which marketing activities and channels have the greatest impact on revenues. Q :Are today’s custom- er analytics tools enabling marketers to work smarter and if so how? Peterson: By an order of magnitude! Customer location analytics is one of the biggest changes in retail in the past 100 years. Online shopping experiences have taught retailers the rich value of detailed purchase path data, and now it is available for in-store behavior – for the first time. With location analytics, a retailer can mon- itor shopping path and behavior through every department, aisle and fixture. The better we know our customer behaviors, the better we can target-market and mer- chandise to meet and exceed our goals. With location analytics, retail leaders look at traffic reactions to merchandising, mar- keting and operational efforts. They look at how they are measured in total traffic, conversion, most trafficked, first visit, re- peat visits, cross-store visits and loyalty, and this goes right down to the departmental, brand or fixture level. Armstrong: Social mobile consumers are very disruptive. When a consumer can walk into a store, take a picture of an item, walk outside and buy it on Amazon, eBay or a competitor’s site it puts a huge amount of revenue at risk. Customer analytics from companies like ours helps retailers defend against this existential threat. Today’s mar- keters need an analytic application suite, not a tool. We do all the integration – both your data and external data – and inject domain expertise and business process so marketers can answer key questions, like: “Who should I be selling to?” “When should I time my next promotion?” “What should the offer be and what are the right channels for this sub-segment of custom- ers?” Marketers can now pinpoint how to increase marketing ROI without worrying about integrating and analyzing the data themselves. Q Are marketing cam- paigns one-off events or is there a way to develop a customer lifecycle approach? Peterson: Through precise location an- alytics, retailers now can monitor the entire path to purchase – from consumers using smartphones to comparatively shop or ac- cess a coupon/promotion, to the way they interact with merchandise in a brick-and- mortar location. With this actionable data, marketers better understand the specifics of what led to the purchase providing the ability to move beyond the traditional blan- keted “campaign” to a year-round interac- tion based on consumer behavior. Custom- ers “opt-in” by downloading a mobile app, and the retailer can push highly-targeted promotions, send information about up- coming sales on merchandise they may have “visited” but didn’t purchase, and of- fer discounts for major events– based on correlations like visits, dwell and intent – to drive sales like never before. Armstrong: Campaign lifecycles are getting shorter and shorter. There used to be a season to campaigns – there was a plan, a budget and then reports on a monthly or a weekly basis. Now, in the holiday season, there are daily and intra- day campaign lifecycles. This is the perfect storm that retail marketers, and really any B2C marketers, face today. This is the en- vironment where customer analytics from companies like ours provide value. Q The goal of retailer investments in cus- tomer analytics is to increase marketer intelligence. How can this best be accomplished? Peterson: E-commerce taught retailers the incredible value of observing the on- line purchase path. Armed with this data, e-commerce divisions constantly modify the online experience to improve shopping performance at every step in the process. The yield and conversion process is incred- ibly effective. Now retailers are applying these location analytics and traffic data lessons in the store to gain powerful shop- per behavior insights and translate them into performance improvement just as it is done online. By implementing an innova- tive, low-cost, easily integrated platform that delivers this rich data from every me- ter in the store, marketers are armed with the business intelligence needed to make informed decisions and ensure a rapid in- crease in key performance indicators. Armstrong: B2C marketers face a lot of data challenges, from new marketing chan- nels to evolving demographics, including younger buyers who are comfortable with technology and expect an omnichannel experience. Traditionally, retail marketers either outsourced the problem of under- standing customer interactions to agencies, consultants or third party database provid- ers to tell them what they should be think- ing about relative to their customer base, or they tried to make sense of data from disparate marketing execution systems. With the evolution that’s taken place in the cloud-based infrastructure and analytic applications, we’ve been able to pull all of this together to help marketers excel at their jobs. RIS 20 N OV E M B E R 2 0 1 3 R I S N E W S . C O M Technologysolutionsguide CUSTOMERANALYTICS
- 3. COMPANY NAME/ WEBSITE RELEVANT PRODUCT/SOLUTION KEY CLIENTS 1010 Data www.1010data.com Market Basket Analysis, Loyalty Card Analysis, Inventory Optimization, Out of Stock Analysis Dollar General, Rite Aid, Vitamin Shoppe Aerohive www.aerohive.com Retail Analytics 7-Eleven, Drakes Supermarkets IBM www.ibm.com Smarter Analytics Barnes & Noble, Dillard’s OfficeMax iInside www.iinside.com Business Intelligence, Mobile Applications, Increased Basket Size, Clienteling NA Lighthaus Logic www.lighthausvci.com Visual Customer Intelligence (VCI) System Champs Sports, Foot Locker Manthan Systems www.manthansystems.com ARC Merchandise Analytics, ARC Customer Analytics, ARC eCommerce Analytics, ARC Store Operations, ARC Human Resource Analytics Canadian Tire, Crocs MicroStrategy www.microstrategy.com Intelligence, Express, Cloud Guess?, Limited Brands, Lowe’s Oracle www.oracle.com Customer Analytics, Merchandising Analytics Burlington Coat Factory, Deckers Outdoor, Finish Line PivotLink www.pivotlink.com RetailMETRIX, DataCLOUD, AnalyticsCLOUD Carhart, Party City, REI Predictix www.predictix.com Forecasting, Planning, Pricing & Promotions Crate & Barrel, dELiA*s, Rent-A-Center QuantiSense www.quantisense.com Decision Orchestration Platform, Q Merchandising, Q Direct, Q Mobile Reitmans, Urban Outfitters, Pac Sun RetailNext www.retailnext.com People Counting, Marketing & Merchandising Cache, Gander Mountain, Gordmans SAP www.sap.com Business Objects, Lumira, Crystal Reports, Predictive Analysis Ace Hardware, eBay, Chico’s FAS SAS www.sas.com Demand Forecasting, Intelligent Clustering, Revenue Optimization Suite, Size Optimization Autozone, Brooks Brothers, Macy’s Teradata www.teradata.com Big Data Analytics, Business Intelligence, Demand Planning Charming Shoppers, Hallmark, Metro Group 22 N OV E M B E R 2 0 1 3 R I S N E W S . C O M Technologysolutionsguide CUSTOMERANALYTICS