Getting Better Analytics And Insights From Your Collected Customer Data

Data collection and analytics are tightly coupled. The mistake we see made over and over again is that companies tend to focus their customer data collection efforts with a single objective (or a single program) in mind. This treats the data collected as a short-term objective, not as a long-term asset. Over time, this results in data islands that eventually “go dark” given that no one is managing customer data as part of an explicit long-term effort.

Have A Long-Term Data Strategy

When it comes to customer data, a long-term data collection strategy almost always proves critical for any advanced analytical work that leads to meaningful business outcomes that can optimize (i.e., simulation management, condition-based maintenance, predictive maintenance and digital twins). Trending analysis, predicting behavior and customer profiling all benefit from long-term data collection strategies. Companies that understand customers’ buying patterns over longer time frames stand to win key insights versus their competitors.

Customer data deserves a data-access-centric strategy to ensure that the data is treated as a reusable asset. This implies that the data should be available to the right people in the company when they need to repurpose it or mine it months or years later. If the data is not findable, threadable (tied to other data sets) or readily accessible, then it’s effectively dark, and its chances of being repurposed are low.

If you are storing your customer data like you store everything else, chances are much of the data you’ve collected from customers has already gone dark. The tendency is to focus on analytical outcomes without preparing the precondition required for the analytics to occur over a longer period of time. If a data strategy for customer information isn’t well-executed, then customer data will reflect the problem you already have in your data center — lots and lots of data sets that represent difficult-to-access data islands.

Thread Your Data

Sophisticated analytical efforts require advanced techniques such as data threading. Threading data across many silos of data is a challenging undertaking. Techniques deployed to achieve threading include (re)ingestion of data, aggregation, parsing, meta data enrichment and indexing. Data is often so extremely siloed that the most efficient first step is simply discovering data islands and recollecting them into an architecture that allows for advanced analytics. The good news is that data capture and storage technologies are relatively cheap, but finding data and then curating it properly does require significant investment.

Customer data needs to be curated and managed as an asset. As more data is collected, it needs to be aggregated with customer data collected during the previous year (or the last campaign, the last payables cycle, etc.).

For example, if a financial institution wants to understand if a customer is approaching a life-changing event such as marriage, having children or purchasing a home, then threading becomes important because it lets you piece together various customer data collection efforts into a single threaded digital dossier. The threaded customer digital dossier allows for different customer data (collected at different points in time) to be accessed for future analytics. It treats customer data as valuable, evergreen and interconnected. A data architecture that allows you to thread and incrementally expand the customer data set is an essential component to making more with your customer data. Advanced analytics, in turn, will allow you make better use of customer data that is properly curated through threading or other data access techniques.

Teamwork

Separating the customer data collection process from the data curation process from data analytics is not a recipe for success. Unfortunately, most companies treat these three activities independent of each other. As a result, customer data is underutilized, undervalued and is not curated as a long-term asset.

The best customer analytics happen when you intersect people who understand the customer data being collected with people who understand how to use and access the data over time. This means that customer data collection efforts need to be discussed in one room with data architecture folks, analytical/data science teams and traditional marketing/customer success teams, ensuring that all have an active voice at the table.

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Apple, Huawei, Amazon Gain in Sluggish Tablet Market: IDC, Strategy Analytics

Apple, Huawei, Amazon Gain in Sluggish Tablet Market: IDC, Strategy Analytics

HIGHLIGHTS

  • Overall tablet sales dropped to 37.9 million
  • Apple saw nearly 15 percent boost in iPad sales from a year ago
  • Samsung remained the number two vendor with a 15.8 percent market share

Apple, Huawei and Amazon boosted tablet sales over the past quarter, despite the ongoing slump in the overall market for the devices, surveys showed Thursday.

Overall tablet sales dropped 3.4 percent from the same period last year to 37.9 million, according to a survey by research firm IDC.

Apple’s nearly 15 percent boost in iPad sales from a year ago gave it a 30 percent share of the global market, IDC said.

IDC said Apple’s gains came from consolidating its lineup and introducing new tablets, including a 10.5 inch iPad Pro, which encouraged some consumers to upgrade.

Samsung remained the number two vendor with a 15.8 percent market shares as sales dipped one percent, the report said.

China-based Huawei meanwhile bucked the overall trend with a strong 47 percent gain in sales, vaulting to the number three position with an eight percent market share, the research firm reported.

IDC estimated that Amazon – whose sales figures are not reported – boosted its Fire tablet sales by 51 percent from last year to capture the fourth spot at 6.4 percent.

China’s Lenovo was fifth with a 5.7 percent market shares as sales dropped 14.6 percent from last year, the report said.

A separate survey by Strategy Analytics estimated a seven percent decline in global tablet sales to 43.8 million units in the April-June period.

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Beyond open data: Insights through analytics

city analysis (Who is Danny/Shutterstock.com)

The federal government is taking big steps to share information and make data more free and open. Thanks to legislation like the Digital Accountability and Transparency Act, agencies are now required to post standardized spending data on the USASpending.gov site. Other initiatives, like the Government Publishing Office’s GovInfo.gov, let citizens use full-text searching and metadata to sift through decades of digitized content. It seems as if we are entering a new chapter of open data. But what, exactly can governments do with this data on hand? How do citizens and public officials make the most of this unprecedented level of access to information?

Analytics are what allows government to use “data as a flashlight, not as a hammer,” according to “A Practical Guide to Analytics for Governments,” recently produced by the team at the SAS Institute and published by Wiley.

The book celebrates information sharing and the wide range of data available on the municipal level in particular — from smart streetlights that also collect info on pedestrian foot traffic to rail equipment outfitted with sensors so that repairs can be made as needed, rather than on a maintenance schedule. (An innovation that Washingtonians inconvenienced by D.C. Metro’s months of “SafeTrack” repairs might envy). Overlaying of municipal code enforcement and police activity data reveals unexpected correlations between property neglect and crime, and having studied algebra in high school is connected to markedly higher income achievement later in life.

“Armed with insights” from shared data, officials in Arizona’s Pinal County used the strength of analytics to more effectively understand already-existing health data in a way that would better protect the public from heat stroke. Investigators were surprised to discover that analytics revealed the highest threat of heat-related illness was not found among the elderly — as had been expected — but instead, among the young people of this Arizona community.

Small agencies can benefit from analytics as much as larger ones.  The book’s authors make the case that smaller cities may be best positioned to take advantage of technology advances because there is “less infrastructure to retrofit.” Since only 300 U.S. cities have populations that exceed 100,000, they add, the opportunities for data-driven innovation are substantial.

State-level open-data success stories are also hailed, most especially the example of  North Carolina, which “opened its 2017 budget for citizen scrutiny” with a new visual analytics tool.

But more important than making data itself available, the authors argue, is recognizing the challenge of melding data into analytics. After all, they assert, “typical government IT projects are built in a siloed approach,” which means that while agencies have torrents of data, often not a drop is shared. Teachers are not given the opportunity to proactively provide remedial attention to students. Police don’t have background information to help them approach a suspect with either greater caution or more compassion.  The book also looks at applications in transportation, public health, child welfare, prescription drug abuse, fraud prevention, and it methodically lays out both the depth of missed opportunities and the possibility of a brighter future.

As government at every level updates its IT assets, the book warns CIOs that “[a]cquiring technology for technology’s sake … rarely achieves the expected outcome.” Instead, the book makes the case that the emphasis should be on “building an analytics-driven government” and leveraging data to “build stronger analytics capabilities.”

“A Practical Guide to Analytics for Government” lives up to its title and concludes with a specific suggested solution. Establishing an official center of analytics, the authors write, can help agencies create a keen awareness of the importance of “building common competency … [that] enhances government analytic success through shared experience.”

Some cities have begun to work in that direction, and the City of Boston’s Citywide Analytics Team and the New York City’s Mayor’s Office of Data Analytics are hailed for seeking “innovative ways to leverage data.”

Such efforts could even unite an otherwise polarized political community, the authors suggest, since “both Republicans and Democrats value opening the public’s business to citizens.” Indeed, they contend that during a time when the citizens increasingly distrust political leadership, “open data can . . . promote legitimacy.”

More importantly, though, the authors stress that governments at all levels should be “breaking down barriers to sharing and accessing information … to ensure frontline workers, management, and policymakers have the knowledge they need.”   After all, as Shawn P. McCarthy, research director of IDC Government Insights, is quoted as saying about this book, “in many ways, modern government is information.”

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Smart Master Data Management Will Power Your Customer Analytics And Insights

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It’s supposed to be a post omni-channel world. A time when personalization is the norm, but as customers we know this is not the case. When customers contact companies, the companies often don’t recognize who the customer is, and they certainly don’t know what the customer’s preferences are. The truth is customers rarely get the personalized and tailored experiences they desire.

Well it’s not a shortage of data about those customers.

So why is it that when customers contact companies, most companies seem completely unaware about anything specific regarding that customer, let alone tastes and preferences. The reason is delinquency when it comes to the aggregation and management of the customer’s data inside the company.

Companies have many challenges when it comes to using customer data, particularly data that is supposed to help the company make better decisions in real-time.

David Rowley, CTO of IAC Publishing Labs, and former executive at customer experience software vendor Sprinklr, said, “the notion of a centralized repository for key business data is an important aspect of providing a more comprehensive customer experience.”

But many companies don’t have this today.

This information is called master data. “Master data” can be about products, employees, materials, suppliers but also may include documents and sales. Rowley said, “A centralized master data store (the repository for the master data) can improve real time decision making and analytics, if information about the entities (e.g. customers) is stored centrally. This provides you with a single trustworthy source of truth about the customer.” Rowley added that you can report directly on that data, rather than having to aggregate customer data from separate systems. He said, “A central view of the customer, for example, keeps various systems in sync with what the customer has done.”

Vic Bhagat, CIO of Verizon Business Services, said, “master data is more important now than ever – the amount of data being generated today will pale in comparison to what will be generated in just a few more years.” He added, “Having a master data management strategy clearly defined — early on — enables a faster approach to analytics to deliver a more proactive, predictive, prescriptive outcome that customers expect today.”

As we established, companies struggle with their current state of their customer data management, but what happens when the business becomes a little complicated, such as with a merger or acquisition. How does the company merge all the new data with all the historical data? Master data management can also help with this.

We addressed the fact that many data systems that aren’t integrated. Sometimes data problems include the actual data sources, data duplicates, no data governance and no standards. Data is not always shared efficiently within the organization. Many of the world’s biggest companies operate like separate islands. Customer service and sales do not share a crm. The company does not collaborate around the customer to ensure a powerful customer experience. The company’s various departments operate in silos. More often than not the people working inside the companies do not even know one another in different departments, let alone use data that spans across the organization.

This is a challenge for organizations who need to create easy and elegant experiences for customers. Companies need to leverage data in real-time to make better decisions. Customer analytics are not helpful when one can’t trust the accuracy of the data. And with many companies today data is often too little too late.

“Leveraging Master data enables enterprises to clean, integrate, and supplement their data to ensure a complete 360 view of their customer. With this view, companies can make informed decisions, align different departments, and create world class online and offline customer experiences leading to sales growth,” says Rishi Dave, chief marketing officer at Dun and Bradstreet, a data and analytics company who provides master data.

End-to-end master data management helps clients make marketing campaigns 30% more efficient, improve upsell and cross-sell rates by 60% and increase loyalty members’ spending by 20%, according to Informatica.

Neopost Uses Master Data For A Transformation Initiative

Neopost, a market-leading global provider of mailing solutions, digital communications and shipping services, found that master data was critical for a transformation initiative. The company wanted a way to more competitively know and serve their customers. As Neopost focused on modernizing its offerings and delivery for the digital age, strong data management has played a key role.

“Besides developing the actual software we sell our customers, we’ve got to build an infrastructure where data quality is paramount,” says Steve Rakoczy, Neopost’s North America CIO. “You can make a mess really quickly in the electronic age if you don’t have your data right.” After using master data, Rakoczy said, “We can successfully manage our Salesforce environment for the first time in over a decade.”

Master data management can help your company create more compelling customer experiences, but first the company must decide on a strong data approach.

For more from author Blake Morgan sign up for her weekly customer experience newsletter here.

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