Laurie Sullivan writes, “Marketers agree customer data drives marketing decisions. Good thing, because advertisers continue to collect mounds of it from mobile, search, social media and more.” [“What To Do With All That Data?” Data and Behavioral Insider, 14 March 2010] As most people are aware, these mounds of collected data have earned the moniker “Big Data.” Although Sullivan insists that using Big Data to make marketing decisions is a good thing, she also points out “that marketers often fail to use this data properly.” She explains:
“In a recent study of 253 corporate marketing decision-makers by Research Now on behalf of Columbia Business School and the New York American Marketing Association, 36% said they have ‘lots of customer data,’ but just ‘don’t know what to do with it.’ Thirty nine percent of marketers admit they cannot turn their data into actionable insight, which presents a major problem. In many companies, the effective use of data for marketing decisions lags behind the desire to do so.”
As I’ve pointed out in previous posts, data (especially mounds of data) is of little or no value if insights can’t be drawn from it or if insights can’t be turned into actions that make a business more effective and profitable. My company, Enterra Solutions, is actively working to help companies in the Consumer Packaged Goods (CPG) industry gain insights that lead to better decisions. Sullivan continues:
“It turns out one of the biggest obstacles to turning data into actionable insight is that companies don’t share data across departments and partners effectively. … In the past the departments like sales, marketing, customer service, public relations, and supply chain typically used their own datasets, keeping the information in silos. The promise of big data analytics is based on the ability to link together the silos to better understand interactions between firm, customer, and business partners. Enterprise companies have been doing this for years. Now it’s time for online marketing to join in.”
In a number of past posts, I have discussed business perils associated with corporate silos (see, for example, The Curse of Silo Thinking, Silos in the Supply Chain, and Breaking Down Silos). One reason that Big Data analytics helps break down silos is that it provides a company with a single version of the truth. As supply chain analyst Lora Cecere likes to say, data should be written once and read many times. Corporate alignment simply can’t be achieved if each division is using its own set of numbers. Sullivan concludes:
“The study also found nearly all corporate marketers participating in the survey — 91% –believe successful brands use customer data to drive marketing decisions. This sentiment remains consistent, with no industry measured below 83%. Among respondents who are at the CMO level of their organizations, agreement rose to 100%. Some 74% of survey respondents admit their companies collect demographic data, 64% customer transaction data, 60% customer use transactional data, 35% social media content created by customers and targets digital data, and 19% collect customer mobile phone and device digital data. 29% said that their marketing departments have ‘too little or no’ customer data.”
Umesh Sonawane writes, “Demand planning is the most critical process in the CPG industry, since it drives all downstream processes (raw material/finished goods inventory planning, procurement planning, capacity planning, manpower planning, transportation planning, etc.) for running the organization in the most effective and efficient manner.” [“Demand Planning in CPG industry – Practising the Best Practices,” Supply Chain Management, 11 November 2011] Demand planning, of course, begins with the kind of Big Data discussed above by Sullivan. Ben Pivar, vice president, Supply Chain Technologies Practice, Capgemini North America Applications Services, states, “Consumer packaged goods manufacturers are being squeezed by trends in customer purchasing and technology, coupled with pricing pressures, higher input costs, and fluctuating commodity prices. In today’s market, CPG companies have to integrate and collaborate with trading partners and better manage retail shelf space. Growing recognition that failure to innovate will result in poor performance, acquisition, or worse, is driving leading CPG companies to transform their operating models.” [“The Demand-Driven CPG Enterprise Sees Maximized Revenue,” SupplyChainBrain, 7 March 2012] Collaboration begins with the secure sharing of sensitive data. Pivar continues:
“To date, the typical focus for retailers and manufacturers has been on reducing overall operational cost. A new, integrated model for managing the shelf is helping CPG manufacturers and retailers to maximize revenue, margins and in-stocks while removing costs across the value chain − the so-called ‘Demand-Driven Enterprise.’ The new model was inspired by recent engagement with a large consumer products company having trouble managing retail customer relationships. Retailers changed their minds about promotions or placed/canceled large orders at the last minute. Resulting supply chain inefficiencies required extensive buffer inventories to support anticipated customer service levels. By integrating retailers’ POS data into the planning process, the company gathered better insights into consumer behavior and worked with retailers to manage orders, promotions and inventory more effectively – ultimately improving inventory turns by 50 percent.”
It has been said that volatility is a cancer that can cripple supply chains. Demand-driven supply chains are designed to reduce volatility while enhancing revenues. Pivar states, “[A] Demand-Driven Enterprise integrates planning and execution more collaboratively across manufacturers and retailers. This drives revenue and margin increases by better supporting assortment optimization, space optimization, trade funds management and demand planning while streamlining the supply chain.” Those are all good outcomes. Pivar also notes that corporate alignment is required to make it all work and must involve “Sales, Marketing, Product Development and Finance.” The power of the Demand-Driven Enterprise, Pivar claims, “lies in five core concepts.” They are:
* Collaborative Category Management — CPG companies have dedicated account teams that work with specific retailers to research consumer behavior, shopper affinity, and new product introductions. By leveraging this data, account management teams can embed category strategies into improved assortment and store-level plan-o-gram recommendations.
* Demand Planning Synchronization — Most CPG companies forecast distribution center shipments rather than actual consumption data at the shelf, causing discrepancies between true demand and available supply. By tapping into POS data directly from the retailer, accurate analytics can be performed by Sales, Marketing and Demand Planning departments and assist account teams to accurately analyze promotion effectiveness.
* Trade Promotion Management and Optimization — The Demand Driven Enterprise drives accurate sales planning, measurably increasing sales through promotion optimization. CPG manufacturers can model the impact of different types of trade promotions to better understand the ROI associated with each trade instrument and better collaborate with retailers for future promotions.
* Inventory Planning Synchronization — Improved forecast accuracy is combined with other key data points from the retailer to calculate time-phased net inventory requirements for each node within the supply chain. By synchronizing inventory strategy and logistics with retail partners, CPG companies better understand true inventory requirements and thereby minimize inventory buffers across the network. Identifying and prioritizing different types of demand signals, and deploying inventory from the most cost-effective supply location becomes simple.
* Supply Chain Execution Collaboration — Retailers and CPG companies must collaborate on-demand and shipment plans within the execution window. Visibility into the retailer’s inventory strategy and POS data enables CPG companies to manage inventory and resource constraints and to produce an executable deployment plan to meet projected consumer demand.
Pivar concludes, “CPG manufacturers using the DDE model benefit from higher service levels, faster new-product introductions, improved margins and faster inventory turns.” He notes that “implementing an enterprise approach can result in benefits in … top-line growth, margin enhancement, efficiency/cost reduction, and working capital improvements.” For most companies, Pivar claims that “moving quickly to a demand-driven model is imperative for maintaining competitive advantage in the crowded CPG marketplace as retailers are beginning to expect and demand these value-added services from their trading partners.” As president/CEO of a company that can help CPG companies become Demand-Driven Enterprises, I couldn’t agree more.
