Veriscope is a Master Data Analytics offering supporting MDM and data governance programs by facilitating crucial analytics and reports. Optimized to offer maximum user control, Veriscope is actionable, configurable, flexible, extendible, and intuitive, built with an easily-navigable user interface and a fully-customizable set of dashboards. It can be installed and configured for your MDM environment in a matter of days – and we back that up with a Proof of Technology.

Reports and Dashboards

Multiple dashboards provide insight into the data hub, like business metrics and trends formaster data change, quality and composition; and operational metrics on error trending, SLA failing, poor-performing transactions and hub workloads.

Pre-configured and dynamic reports can be generated on-demand or automatically scheduled and include Data Change Trend Analysis, Data Quality Analysis and Data Load Results, to name a few.

Learn more at http://www.infotrellis.com/veriscope/

Big Data Enables CPG Companies to Gain an In-Depth, Personal Connection with the End User

This article was featured in the Q3 2014 edition of Loyalty 360‘s Loyalty Management magazine.

Consumer Packaged Goods (CPG) companies have accepted for many decades that the reality of the industry is that the customers are interacting with intermediaries like digital merchants and retail outlets, not directly with them. The store gets to develop the relationship with the customer and the CPG company has to bridge a bigger gap, targeting end-users with broad strokes like TV commercials or billboards.

It’s hard to develop a sophisticated targeted marketing campaign or a customized loyalty offering, after all, when all of the customer data is being generated by the customer-store relationship, not the customer-product relationship. Stores typically have little incentive to offer detailed information about sales and other interactions to CPG brands – they naturally prefer consumers to be loyal to the store rather than loyal to the product brand names sold within, especially if the store offers their own branded products.

Ultimately, it can be tricky to make a connection when there’s a middle-man between you and your customer.

Not being able to easily connect has presented a number of challenges for the CPG industry in particular.

One of the overarching challenges is related to product development and promotion: a limited understanding of the customer can lead to imperfect offers and imperfect promotions.

That limited understanding is typically achieved through market research. CPG companies had to find alternative ways to gain insights about their target markets. Focus groups, surveys and coupon campaigns are costly and are all in some way imperfect (they provide limited data; they are based on small sample sizes; they are often not very timely etc.).

Big Data has the potential to change all of this. By analyzing millions and millions of social media comments, CPG companies are able to identify who purchases and uses their products. They can also determine the profiles of those consumers: what are their hobbies, what are their favourite TV shows, what initiatives resonate with them and are important to them?

It’s been said that social media networks are the ultimate focus group. It’s instant, uncensored customer feedback at a massive scale – and the ability to harvest this data and crunch it for analysis is providing CPG companies with a level of insight that was unimaginable just a few decades ago.

Not only is the customer feedback finally directly accessible, social media and other digital communications provide channels through which the CPG companies can speak directly to individual customers, bypassing the store entirely. This allows them to nurture relationships with end-consumers well beyond “hoping they see the billboard for the new cereal on their commute to work”. This opens the door to actual relationship-building tactics that companies in other industries have been using for years but have traditionally been unworkable for CPG.

Which leads me to the main question I want to pose:

Can investments in Big Data capabilities make direct customer loyalty or CRM programs achievable for CPG companies?

Historically, the Consumer Packaged Goods (CPG) industry didn’t see much potential in traditional loyalty or CRM programs; because the retailers selling their products were the ones interacting with the end consumer, it was hard to reach out to, establish, and then nurture relationships with individual buyers.

“Traditionally, CPG brands have few options when it comes to impacting purchase behavior in third-party retail environments, other than relying on costly in-store displays to grab shoppers’ attention. They also miss out on direct access to purchase data, which makes it difficult to know which marketing levers they can pull to get more of their brands into the shopping basket at checkout.” (Punchtab)

Both CPG executives and expert industry observers have expressed skepticism in the past that a traditional loyalty program is a good fit for CPG.

“Consider the average loyalty program pays out under 2% for every dollar you purchase,” Jason Dubroy, VP managing director, Shopper DDB says. “Someone buying a $5 box of cereal [will get] less than $0.10 [from the] loyalty program. People may eventually realize the effort for them to enter 50 pins isn’t worth the value of the program.” (Strategy Online)

It’s true that attempts at CPG loyalty programs in the past have proved too high-friction for consumers to really engage much with them. That isn’t the case anymore. As social media moves from being just another advertising platform to a being potential source of data and two-way customer communication, giving CPG brands the direct access to consumers that was once unattainable, industry leaders are considering the possibilities for a shift in their attitude towards loyalty.

“There is an unprecedented opportunity for CPG companies to begin building deeper and more profitable relationships directly with consumers. Whereas loyalty programs were traditionally used by companies who owned the point-of-sale, today CPG marketers are able to leverage loyalty and analytics software to recognize & reward loyalty in an entirely new way.” (Crowdtwist, 2)

Again, social media and other sources of big data about the customer offer the opportunity to avoid relying on the retailer, which has long been regarded as one of the biggest road blocks to CPG customer analytics at the level of the individual.

The move into the loyalty space would offer up CPG [companies] more transactional data to deal with (cutting out the middle-man retailer, Dubroy adds). “Retailers traditionally can give CPG companies about as much info as a bouncer [does] about who is at the party,” Sarna says. “Loyalty is much more like being the socialite who can wander around, who knows everyone and what they’re thinking.” (Strategy Online)

The ability to finally isolate the individual and cater to them is an exciting and relatively new opportunity for the CPG industry.

“By being able to aggregate and attribute engagement, social activity and spend back to individuals, loyalty programs offer CPG marketers the ability to finally identify which of their efforts are most effective at stimulating consumer behavior and converting people along their path-to-purchase.” (Crowdtwist, 6)

CPG companies have already started experimenting with social and mobile targeted personalization of advertising and communications.

“One of the challenges with mobile is that it is such a personal device and impersonal messaging, whether is push message, an ad, or an offer, it doesn’t matter what it is or who it is coming from it is not well received,” John Caron, vice president of marketing for Catalina Marketing. “We know to how to leverage historical purchases, data and analytics to be able to identify and drive the right campaign in order to allow that to define the media that you see in app or mobile web on your smartphone,” he said. (Mobile Marketer)

Indeed, the key word here is “personalization” for many of the emerging use cases that combine Big Data, loyalty and the CPG industry. How are they crafting the profiles and personas that they use to customize messages and offers for customers? The answer to this lies in linking internal and transactional data with external and social data; once a company has the power to match each real-life consumer to their online identity, they unlock a wealth of data that allows them to treat those individuals with a high degree of personalization.

“By combining consumers’ digital and social profiles and behaviors with real purchase data, CPGs have the ability to understand which online behaviors increase awareness, trial, preference and overall buy rates, while optimizing marketing effectiveness by focusing on the channels that matter most.” (Punchtab)

What does “personalization” mean in terms of tangible steps a business can take? Bazaarvoice proposes:

“A brand can pre-sort reviews on product pages based on information gathered on the visitor. For example, a college student may see reviews first from other consumers identified as students. As you learn more about a shopper via purchase history, mobile app usage, online feedback, and the interest graph, tailor experiences to that individual. Use dynamic display and mobile ads to serve products the shopper is likely to enjoy alongside opinions from people with similar needs and tastes.” (Bazaarvoice, 4)

Using customer data for the purposes of microsegmented marketing messages is a well-documented use case, but only recently has the data needed for this level of sophistication been accessible to the CPG industry. It’s a strategy that has been proving its ROI for a few years already.

“In a recent study involving more than 300 CPG brands and 80 companies, Nielsen reported that “CPG brands can experience a return of almost $3 in incremental sales for every dollar spent on online advertising that has been precisely delivered using purchase-based information”. When you consider the potential impact that better sources of data can have for an industry that spends more than 25% of the global advertising budget, the implications are astounding.” (Crowdtwist, 4)

Beyond just more targeted advertising, the introduction of a loyalty or CRM program that works on the same principle of personalized offers and intelligent audience engagement has the potential to drive increased revenue beyond any one promotion or product.

“CPG companies can develop umbrella loyalty programs across their entire family of brands, rewarding consumers when they buy and engage with any of the brands in the portfolio. These programs can be highly effective in increasing trial, driving preference, and boosting cross-category purchase: a recent PunchTab survey showed that 73 percent of moms would be interested loyalty programs for a parent company, and 59 percent of moms would buy other products from the parent company if doing so resulted in more loyalty points — with 46 percent indicating they would even switch from a competitor’s product.” (Punchtab)

So what’s the potential payoff for pioneering CPG companies that launch loyalty programs using all this new customer data?

“Approximately 50% of people who enroll in a CPG loyalty program remain actively engaged with that brand on a monthly basis, and they interact with the brand 2.5x more often than the brand’s average consumer.Members who enroll in a CPG brand’s loyalty program are more likely to open branded emails, and are more likely to clickthrough on emails that contain a call-to-action. On average, our CPG client partners have experienced an increase of 109% in email open rates and a 25% increase in click-throughs for their program members.” (Crowdtwist, 3)

The opportunity is clear; Big Data technology, enabling companies to gather social data and accurately match it to transactional data, may be the missing piece that makes investing in a loyalty program feasible and advantageous for companies operating in the Consumer Packaged Goods industry.

I argue that loyalty for CPG companies has never been intuitive or a perfect fit in the past, but it is now. In some respects, the CPG industry has the advantage of starting late – as they have no legacy systems to deal with, they have a clean slate. Companies investing in loyalty now will be doing it with 20+ years of established best practices in one pocket and the incredible technology available today in the other.

Perhaps even more importantly, companies can come at this with fresh eyes and a willingness to think above and beyond how things “have always been done”, because so few CPG companies have ever done much in the loyalty or CRM space before. Creativity, cleverness, and the ability to make full use of the tools and information available is a potent combination.

CPG companies are in an interesting position to potentially go from rarely bothering with loyalty to completely revolutionizing what can be accomplished with a loyalty or CRM program in a very short span of time.

Whether or not they will is another question entirely.

–

InfoTrellis Inc. is the creator of Customer ConnectId™, the Big Data solution that provides a deep understanding of customers using patented identity resolution and data matching technologies. Customer ConnectId™ is enabling CPG companies to gain an understanding of their customers in ways which were not possible before for applications like micro segmentation, CRM or loyalty program initiatives.

 Learn more at http://www.infotrellis.com/big-data-enables-cpg-companies-to-gain-an-in-depth-personal-connection-with-the-end-user/

Top Reasons an MDM Implementation Fails

I often become involved in an organization’s MDM program when they’ve reached out to InfoTrellis for help with cleaning up after a failed project or initiating attempt number X at achieving what, to some, is a real struggle. There can be a lot of reasons for a Master Data Management implementation failing, and none of them are due to the litany of blame game reasons that can be used in these scenarios.  Most failures arise from common problems that people just were not prepared for.

Let’s examine some of the top reasons MDM implementations fail. In the end they probably won’t surprise you, but if you haven’t experienced it yet you will be better prepared to face them if they happen.

Underestimating the work

I am starting with this one because it leads to many of the others, and is a complex topic. It seems like a simple thing to estimate the work but there are a lot of aspects to an MDM project that aren’t obvious that can severely impact timelines and your success.

“It’s just a project like any other”

Let me start by saying MDM is not a project, it’s a journey, or at the very least a program.

Most organizations thinking about implementing MDM are large to global companies. Even medium sized companies that started small and experience growth over time have the same problems as their global sized piers.  While the size of the chaos in a global company may seem much larger, they also have far more resources to throw at the problem than their smaller brethren.

If we stick to the MDM party domain as a point of reference (most organizations start here with MDM), the number of sources or points of contact with party information can be staggering. You may have systems that:

• Manage the selling of products or services to customers
• Manage vendors you deal with or contract to
• Extract data to data warehouse for customer analytics and vendor performance
• HR systems to manage employees who may also be customers
• Self-service customer portals
• Marketing campaign management systems
• Customer notification systems
• Many others

A lot of large organizations will have all of these systems, each having multiple applications, and often multiple systems responsible for the same business function. So by now you are probably saying, yes I know this, and…?  Well your MDM “project” will need to sit in the middle of all of this, and in many cases since many of these systems will be legacy mainframe based systems, you will need to be transparent as these systems won’t be allowed to be changed.

MDM can be on the scale of many of the transformation programmes your organization may be undertaking to replace aging legacy systems and moving to modern distributed Service Oriented Architecture based solutions.

Big Bang Never Works

Now that we have seen the potential size of your MDM problem, let me just remind you that you can’t do it all at once. Sure you can plan your massive transformation programme and execute it – but if you have ever really done one of these, you know it’s a lot harder than it seems and that the outcome is usually not as satisfying as you expected it to be.  You end up cutting corners, blowing the budget, missing the timelines, and de-scoping the work just trying to deliver.

What is one of the typical reasons this happens on your MDM transformation project?

You Don’t Know What You Don’t Know

You have all these systems you are going to integrate with and in many cases you are going to need to be transparent in that those systems may not know they are going to be interacting with your new MDM solution. You are going to need to know things like:

• What data do they use?
  • How often?
  • How much?
  • When?
• Do they update the data?
  • How often?
  • How?
  • What?
• Do they need to know about changes made by others?
  • How often is the change notice required?
  • Do they need to know it’s changed, or what the change was?

This type of information seems pretty straight forward. I haven’t told you anything you probably didn’t know, but, when you go to ask these questions, the answer you will mostly likely often get is:

“I don’t know.”

Ok, so the documentation isn’t quite up to date, (I am being kind), but you are just going to go out and find the answer. Which leads to the next problem.

Not Enough Resources

So this is an easy problem to solve. I’ll hire some more business analysts, get some more developers to look at the code, get some more project managers to keep them on track.  Seems like a plan, and on the surface it looks like the obvious answer, (ignoring how hard it is to locate available quality IT people these days), but these aren’t the resources that are the problem.

You don’t have enough SMEs.

The BA’s, developers and others are all going to need time from your subject matter experts.  The subject matter experts are already busy because they are subject matter experts.  There typically aren’t enough of them to go around, and if you have a lot of systems to deal with, you are facing a lot of IT and business SME’s.

What your SMEs bring to the table is intellectual property. Intellectual property is critical to the success of your implementation.  You will need the knowledge your SMEs bring on your various systems, but there is another kind of intellectual property that you are going to need and can be tied to a very lengthy process.

Data Management through Governance

In order to be able to master your information, you will need to amalgamate data from multiple sources and both the meaning and the use of that information will need to be clearly defined. What may appear to be the same information from one source may have a different meaning.  Data governance is a key requirement to be able to establish the enterprise data definitions that are crucial for your master data.  Even in mature environments this can be a challenging task and can consume significant time and resources.

Data governance may seem like a problematic and time consuming exercise but it is an effective tool to use against one of the other major hurdles you will face in trying to establish a common set of master data.

That’s My Data

Many organizations are organized into silos. The silos are designed to look after their own interests, funded to maintain their business goals and competitive for resources and funding.  While the end goal of any organization is the success of the organization, the silo measures its success in terms of itself.

An MDM implementation is by nature at odds with the silo based organization as master data is data that is of value to a cross section of the business and thus spans silos. The danger in many organizations is that a particular silo has significantly more influence than another, often laying with the revenue generating lines of business.  This over balance of power can easily lead to undue influence on your master data implementation, making it just another project for division X, instead of an enterprise resource to be shared by all.

Data governance is one of the key factors to help keep this situation in check. Your data governance board will be comprised of representatives from all stake holders, giving equal representation to all.  The cross organizational nature of data governance is also the reason that decisions can be a difficult and lengthy process as it requires consensus across all the silos.

Aside from enterprise data definitions, another important aspect of master data management is the establishment of business rules.

Too Many Rules

The business and data governance will need to be involved to establish business rules for:

• ETL processes to loading data into your MDM application
• Updates to information from multiple sources
• Matching rules
• Survivorship rules

The establishment of rules is designed to address one of the big problems MDM is meant to solve: data quality. Organizations will want to manage both data quality on load and ongoing data quality.  One of the big mistakes often made is to try and introduce too many rules right away.

The use of too many rules early on can have a significant impact on the initial data loads into your MDM solution. You are ready for production and most likely getting your first crack at live data to only find out vast numbers of records are being rejected due to your business rules.  Your data loads have now failed and you need to go back and rethink your rules, revise your ETL process and try again.

You finally get your data loaded and your consumers have arrived to start to use the data and your legacy transactions are failing. Why are they failing? Because the application isn’t validating the input according to your business rules, or collecting enough information to satisfy the rules.

Of course there is one way you could reduce this risk, but it often isn’t done well enough and sometimes isn’t done at all.

What Profiling?

Data profiling is the one task that is critical to understanding what your data looks like and what you need to plan for. There are often many barriers to profiling because your party master data will likely contain personally identifiable information (PII) and access will be restricted for security reasons.  You have to overcome these barriers because data profiling is the only way to foresee the gotchas that are going to put you far off track down the road.

Data profiling can be a significant task as each source system needs to be profiled. As you learn more about your data you will have more questions that need to get answered.  All this profiling takes time and most likely needs the time of specific resources as they are the only ones that have access to the information you require.  (There’s that resource problem again.)

Project Management is my Problem?

So far you haven’t heard any magical reasons as to why your MDM implementation should fail. In fact  many of the problems seem to be tied to the typical reasons any IT project can fail:

• Underestimating the work
• Not enough resources
• Trying to do too much at once (including scope creep)
• Time required for discovery

An aspect of an MDM implementation that may be a little non typical includes the need for data governance. Data governance not only gives you the enterprise view of the information you are trying to master, but can also be an effective way of dealing with competing agendas between silos.

Data governance is also one of the key success actors for the ongoing success of your implementation. Since MDM is a journey not a project, longevity is a characteristic of a successful implementation.  Once you have delivered your foundation, the succeeding phases will build upon the base and provide more coverage of your master data.  To ensure the ongoing success of your implementation you will need the support of data governance, to ensure that new systems and upgrades to existing systems use the master data and don’t just create islands of their own.

In the past we tried to achieve what master data management promises today, but with a lack of controls and governance, we ended up with the data sprawl we are trying to correct with MDM. Once the project is over, the role of master data management does not end.  It is important to recognize that you must establish the processes and rules to not only create the master data store, but also to maintain it and integrate it into your systems.  Master data management is not about the installation and configuration of a shiny new software product.  The product is an enabler making the job easier.  The establishment of rules, governance processes and enforcement are what will bring you success.

One final thing that every master data management implementation requires, and you are pretty much doomed to failure without, is strong executive sponsorship. Your MDM implementation is going to take years.  You will require consistent funding and support to be able to take the journey and only an executive can bring that level of support.  Organizations that are organized into silos often don’t play well together, and while data governance can help in this situation, the time may come when a little intervention is required to ensure things keep moving in the proper direction on the expected timelines.

Your executive is a key resource in and out of the board room.  In the board room you will need t champion that has the vision of what your MDM implementation is going to bring to the organization, and keep the journey progressing over time.  Out of the board room you will be faced with competing agendas, data hoarding, shifting priorities, and silos trying to work together.  The executive influence here can be used to make sure that everyone continues to work towards the common goal, and provides the resources required to achieve the gaols in a reasonable time line.

Reference: http://www.infotrellis.com/top-reasons-an-mdm-implementation-fails/

What are the characteristics of Big Data as a Service?

With rich experience in data management for more than a decade, InfoTrellis is pioneering big data management. Traditional data processing techniques are proving to be inadequate. We have the business acumen and technical expertise to provide the best-in-class solutions for handling big data.

Our Big Data Services Include:

Big Data Advisory

• Leads you from Proof of Concept to Production implementation
• Evaluates, models and incorporates the latest tools and technologies
• Suggests a right fit of technologies based on requirements and budget

Architecture Consulting

• The right architecture that suits your current needs and can be extended to fit your future needs
• The right integration plan with your existing technology stack to minimize risk and align with cost and business strategy
• Architecture consulting for your real-time data processing needs and batch processing