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.Read More

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

October 2014 Tradeshow Attendance Schedule

It’s that time of year again! InfoTrellis will be attending and exhibiting at two major tradeshows this 2014 as the weather starts to get a little chillier. If you’ve ever wanted the chance to chat with one of the brilliant brains behind Customer ConnectId™, ask the hard questions about Big Data integration from someone who can actually give you an answer, or just wanted to learn more about the only Master Data Management SI in the industry with a 100% success rate, here’s your chance to meet with us in the flesh.

GTEC 2014

October 27^th– 30^th, Ottawa, ON

Booth Number 611

 

GTEC (Government Technology Exhibition and Conference)  is the primary forum where government and private sector communities gather to exchange ideas and advance the business of ICT in government.

IBM Insight 2014

October 27th – 30^th, Las Vegas, NA

Booth Number 207

Previously known as IBM’s Information on Demand, IBM Insight is bustling with purveyors of cutting-edge technology, and InfoTrellis is delighted to yet again have the honor of being among their numbers. With the growing emphasis on Big Data analytics, we’ll have a lot to say and will certainly be running demos of our own solutions in the space all throughout the show.

We hope to see you all there, and if you plan to attend and would like to set up a meeting in advance with one of our executives, please send me an email at lauren@infotrellis.com to arrange a time for a discussion.

Retailers’ Successes and Struggles with Big Data in 2014

Recent research by McKinsey and the Massachusetts Institute of Technology shows that companies that inject big data and analytics into their operations outperform their peers by 5% in productivity and 6% in profitability. Our experience suggests that for retail and CPG companies, the upside is at least as great, if not greater.”
Peter Breuer, director of McKinsey & Co.’s retail practice in Germany
With November half over and 2015 starting to peek at us over the horizon, we decided it was time to take a look at a few examples of what retailers have been using big data for in 2014. Here are three examples of use cases for Big Data in retail that have emerged in the last year, followed by a few InfoTrellis predictions about what will happen next in the new year when it comes to the evolution of how companies are implementing their Big Data strategies.

Macy’s

Personalized Marketing

The main goal for Macy’s CEO, Terry Lundgren, is to offer more localized, personalized and smarter retail customer experience across all channels. They use Big Data among others to create customer-centric assortments. They analyse a large amount of different data points, such as out-of-stock rates, price promotions, sell-through rates etc. and combine these with SKU data from a product at a certain location and time as well as customer data in order to optimize their local assortments to the individual customer segments in those locations.

 In addition to that, Macy’s gathers, and of course analyses, a vast amount of customer data ranging from visit frequencies and sales to style preferences and online & offline personal motivations. They use this data to create a personalized customer experience including customized incentives at checkouts. Even more, they are now capable of sending hyper-targeted direct mailings to their customers, including 500,000 unique versions of a single mailing. The results are compelling; Macy’s e-commerce division alone has witnessed a growth of over 10% and an overall annual revenue growth of 4% with the use of Big Data Analytics.
 (Macy’s Is Changing The Shopping Experience With Big Data Analytics)
 
Personalizing the user experience is a ubiquitous use case for big data, so it’s exciting to see a retailer actually implementing the technology to accomplish and prove out the value of this marketing strategy. Four percent growth is nothing to scoff at; this number represents millions of dollars on pure profit they didn’t have before. For companies that still believe they can accurately segment their hundreds of thousands of customers with fewer than a hundred profile archetypes, this is an undeniable piece of proof that they may need to consider getting on the bandwagon if they don’t want those millions of dollars to be coming out of their share of the customer’s spending habits.

What’s more, this is a front-and-center application of big data that is highly visible to the shopper. Whether or not they can articulate the difference in quality of experience they get from a retailer that uses it and a retailer that doesn’t, it’s a difference they can intuitively feel and will definitely react to by rewarding one store with loyalty over the others.

Once companies start pulling social data and combining it with internal customer data, their targeting and micro-segmentation capabilities will enable even more uniquely tailored marketing and customer experiences. So long as companies remember that the purpose of this data-collection is to minimize friction and irrelevant messaging for their customers and never to manipulate them or milk them for money like a mindless herd, the consumer stands only to benefit from the evolution of this practice.
For this reason, my prediction is that this will be a big differentiator in the coming years as the companies that experiment with it first (i.e. the early adopters) get better and better, making the gap increasingly noticeable to the end consumer. There will be a scramble by the companies that lagged behind to try to catch up, and this will represent a big shift for the retail industry’s established best practices in much the same way the idea of the loyalty program and the digital storefront did.

Read More http://www.infotrellis.com/retailers-successes-and-struggles-with-big-data-in-2014/

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.

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

The Christmas Shopping Big Data Use Case

There’s a metaphor I like to use about public washrooms. Have you ever been in a public washroom where the toilet flushes automatically, the soap dispenses automatically, and the water turns on and off automatically, but then the drier is manual, and it seems really jarring and weird because you stick your hands under it expecting it to be automatic too and then nothing happens? That’s what’s going to happen to digital customer experiences and marketing best practices.

Let me elaborate.

Say it’s around the second week of December. I’m working on doing my Christmas shopping still, like many people are at this time of year. I open up an email from a large bookstore chain that I happen to have a loyalty card with – one of the few I actually use and carry around with me, and tolerate the promotional emails from. In the email is an offer that says “Got friends around the world? Check out with this coupon and we’ll ship to three different locations for free when you spend more than $100!”

For me, I’d be thinking: “Holy smokes, that’s perfect!! I have lots of friends around the world! I would love to be able to ship to three different places in one purchase! That’s so convenient!”

That might not be something that would excite you, but that’s why (although I’m not aware of it) I got this email and you didn’t. It’s tailored specifically to me because they know this is an extremely relevant offer that will motivate me to make a large purchase.

So I click to get the coupon and it takes me to a “gift suggestion” page. And somehow, it’s only showing me gifts and books that my friends and my family would like. It’s got science humour books, nerdy video game related books, and even suggests a book with big glossy pictures of cars for the two people on my list of ten loved ones that really dig cars. Me personally, I don’t like cars that much – but this isn’t a list tailored to me anymore, it’s tailored to the people I most care about and would likely spend more money on a gift for.
So here I am, sitting at my computer and thinking “WOW that is perfect for this person I care about, this one here is perfect for THAT person I care about, look at this I’m going to get all my shopping done in one afternoon,” and before I know it I have $250 of things in my basket.

 Read More http://www.infotrellis.com/the-christmas-shopping-big-data-use-case/