Posts Tagged ‘location intelligence’
Tuesday, November 3rd, 2009
By Sebastien Rancourt
Canadian privacy laws set ground rules on how organizations may collect, use and disclose personal information. Under the Personal Information Protection and Electronic Documents Act, for example, personal information can only be collected when it is gathered with the knowledge and consent of the consumer-and only used for the reasons for which it was gathered.
Despite these data challenges, marketers and strategic planners have found effective ways to understand customer needs and create actionable customer segments. These insights and best practices-while particularly germane in Canada-are relevant to anyone looking to improve results by targeting more effectively.
Today’s leading solutions begin with geo-demographic clusters. While cluster segmentation strategies have existed for decades, contemporary clustering methods use robust statistical data and advanced analytical power to capture, create and measure more precise customer segments based on geography, demographics and lifestyles. With the right data and analytical tools, organizations can characterize the behavior of every clustered customer-from their favorite movies and foods to their preferred attire and avocations-enabling users to more accurately predict customers’ responses to every campaign.
Professionals in retail, financial services, media planning, real estate and restaurants, among others, rely on cluster segmentation to improve decision making and business results. Yet with the enhancements made in recent years, some marketers have yet to incorporate the latest advances which can boost overall performance. In speaking with experts across Canada, we’ve identified a series of best practices to help guide your next steps.
Segment by neighborhood, not postal codes. Some segmentation strategies rely on postal codes, which can lead to problems down the road. Each month, as many as 5% of the roughly 850,000 six-digit Canadian postal codes change, as Canada Post updates this system solely on the basis of their mail delivery needs. Not only does this taint campaigns in the short-term, it makes it nearly impossible to manage year-over-year modeling and analysis.
The best neighborhood segmentation clusters begin with census data at the dissemination area levels-which are the lowest levels for which reliable census data are published-providing hundreds of reliable data variables. In addition to data accuracy, these neighborhood-based models offer year-over-year consistency, so marketers can build on past success over time.
Incorporate household-level insights. This past year, leading cluster models have found ways to use more comprehensive household level data, incorporating consumer information that goes far beyond census findings. These inputs, which conform to Canadian privacy laws, represent an unprecedented level of detail and behavior-based data-and create a more high-definition view of customers and prospects.
Maximize data points. Not all household level data is the same. Some cluster models are built extrapolating data from as few as 8,000 surveys across the full population of 33 million Canadians. More reliable cluster models will analyze self-reported data from as many as 10 million individuals-providing for more accurate targeting and a lot less guesswork.
Overall, organizations that employ these best practices will benefit from a multidimensional framework that makes it possible to sort through the complexity of Canadian consumer culture without having to manipulate literally hundreds of census and survey variables.
One such solution is PSYTE HD, the Pitney Bowes Business Insight segmentation system created using an innovative two-step clustering process. The 59 clusters identified, including Canadian Elite, Joie de Vivre, Urban Verve and Next Gen Rising, leverage the largest and most robust repository of Canadian consumer intelligence to date-making it easier for organizations to locate new opportunities, connect with customers and communicate more efficiently. We invite you to learn more and look forward to your feedback.
Thursday, October 15th, 2009
Al Beery and Brian Hill, Pitney Bowes Business Insight
Over the next few months, consumers will head to the malls, superstores, and in increasing numbers, to their laptops—and retailers will be looking for any edge they can find to increase sales and margins during this holiday shopping season.
Given the sluggish economy, cost pressures and changing consumer behaviors, there has never been a better time to leverage Location Intelligence in your business. Retailers, manufacturers and shippers will find ways this year to move product to more people in smart, cost-effective ways by analyzing the relationship between distribution centers, retail sites, critical customer segments and household locations.
This is especially critical in light of expected shifts in customer behavior. The down economy means that many customers are buying less, they’re more price-conscious, and they are more selective about what they buy. Many customers are also buying more online—increasing the role of logistics and fleet management,
Using location intelligence to chart how these trends are impacting your business is often the key to greater profitability. Better Location Intelligence can help you to:
• Better project performance of existing retail sites
• Determine optimal locations for new retail sites
• More effectively allocate marketing dollars
• Chart more efficient delivery routes
• Reassess distribution-center locations in light of the increased proportion of direct-to-consumer shipping
In fact, companies that invest in top-quality location intelligence solutions often see positive ROI inside of six months. And many achieve a six-figure return on their investment within the year. Add in the intangibles—happier customers, happier delivery people, and happier customer-service personnel—these all result from greater efficiencies, better communications, and better information sharing throughout your organization.
More information on how Location Intelligence and other data-quality improvements can enhance day-to-day and long-term business performance are available in our White Paper Special Delivery: Just-in-Time Savings or by speaking with your local PBBI representative at 800.327.8627 or via email at email@example.com.
Thursday, September 24th, 2009
by Brian Diepold
Cross posted at http://analytics.pbbiblogs.com/
Among other effects, the current recession is likely to have an immediate and lasting impact on the branch deployment strategies in our industry. The immediate impact is fairly easy to predict. That is, net branch growth rate will decline significantly, most likely with some contraction over 2009 and 2010. But, what should we expect to see happen after the recession?
In the period just after past recessions, we have experienced a short-term spike in branch growth, likely due to some catch-up effects, followed by a return to the normal trend. It would be easy to assume that we could be in for the same kind of response after this recession.
But, I think there are several factors working against a return to the old patterns of branch growth. Most importantly, we have the ever-present alternative channel argument. While the maturing of remote banking may play a role in future branch growth, I believe that the dominant effect will be driven by overall residential development patterns.
If we look at the pockets of high branch growth over the past decade, much of the net new branches have logically followed the suburban development patterns. With every new McMansion development, branches followed to serve those communities. Unfortunately, many of those communities are being hit the hardest by the collapse of the real estate market. Prices are dropping much faster in the outer fringe development than they are in the urban core in many places. One could argue that these developments represent much of the excess inventory in the residential housing market today. As a result, it’s unlikely that we will see more of these developments popping up any time soon.
As the real estate market corrects itself, one of the numbers that is going back up is the percent of the population that rents instead of owning a home. Renters tend to reside closer in to the urban core in more densely populated parts of the market. Coincidentally, banks already have mature branch networks in these parts of the market. That is not to say there will be no new branches built, but simply that the decisions may move towards relocations, renovations, and need-based in-fill of the network, rather than continuing to grow with the residential development.
This is a unique recession, and as a result there will be unique events that unfold during the recovery period as well. One of them, I believe, is going to be a modest transformation of residential development patterns. We should see a move back towards more densely populated residential development. I don’t expect this to be a radical change, but as it changes on the margin, that should have an impact on where we look for new branch opportunities.
The result for branching is a new normal that probably doesn’t include a return to steady branch growth. There will be some branch growth, but I expect it to be more in line – finally – with household and population trends.
Tuesday, September 8th, 2009
By Steve Seabury
While the 2010 U.S. Census is still months away, a recent advance in data analytics demonstrates how amazing things can happen when customer and location intelligence comes together.
For years, real estate specialists and strategic planners have relied on spatial analysis to make decisions that required significant investments. The power of location intelligence proved invaluable on many fronts. The stability of neighborhood demographics enabled decision-makers to hone in on trends that could impact long-term profitability. The precise nature of geocoding provided for year-over-year consistency. Plus, the ability to visualize and map customers, prospects and competition against existing and planned sites let to key insights… insights that have enabled banks, retailers, utilities and many other industry executives to exceed expectations.
At the other end of the spectrum, marketers turned to household segmentation models. Robust demographic data at the household level could be used to create clusters—segments of consumers who shared similar lifestyles, characteristics and needs. This lifecycle approach made it easy to target the ‘retired affluent’, ‘young families’, ‘single post-grads’ and dozens of other key markets. And with records updated quarterly (or even more frequently), marketers could respond quickly to life events.
Now for the first time, these distinct approaches have been combined to deliver enhanced network performance management and customer analytics solutions. Using deeper, more precise demographic data, organizations can make more informed and timely decisions about critical real estate and marketing initiatives. These next generation demographic data tools incorporate advantages from both disciplines and can help organizations overcome today’s top challenges, for example:
- Enables marketers and strategic planers to work from the same platform
- Compares changes in household make-up with neighborhood shifts to uncover pockets of opportunity
- Normalizes household data to block out the noise of short-term events to create more accurate projections
- Eliminates the need for ZIP Code targeting, which rarely reflect true neighborhood and lifecycle segments
- Links store network performance with customer relationship management strategies
Of course, creating the best of both worlds requires you to start with the best in both worlds. That’s why Pitney Bowes Business Insight teamed up with the Gadberry Group and Acxiom® Corporation and their PersonicX® segmentation system.
These data sources compile consumer data from over 100 sources, including public records, the U.S. Census and self-reported data. Measurements for accuracy and completeness are part of a sophisticated multi-source build process where individual data attributes are compared across multiple providers. While mapping and analytic tools previously dealt with neighborhood and block-level data, these new tools drill down to race, ethnicity, gender, education, marital status, occupation, income and lifecycle on an individual household level.
In many ways, incorporating Gadberry and Acxiom data into PBBI predictive analytics models will enable organizations to bridge the gap between real estate decision-making and marketing strategy – incorporating the best of both.
For more information on the newest technologies, visit http://go.pbinsight.com/household-derived-demographics.