Category Archives: Data Quality
Nine years ago when I started in the data integration and quality space, data quality was all about algorithms and cleansing technology. Data went in, and the “best” solution was the one that could do the best job of fuzzy matching the data and cleaning more data than the other products. Of course, not data quality solution could clean 100% of the data so “exceptions” were dumped into a file that were left as an “exercise for the user” to deal with on their own. This usually meant using the data management product of choice, when there is nothing else…. Data goes into a spreadsheet, and then users would remediate the mistakes by hand in the spreadsheet. Then someone would write an SQL query to write the corrections back into the database. In the end, managing the exceptions was a very manual process with very little to no governance to the process.
The problem with this of course is that for very many companies, data stewardship is not the person’s day job. So if they have to spend time checking to see if someone else has corrected an error in the data, or getting approval to make a data change, or spending time then consolidating all the manual changes they made and then communicating those changes to management, then they don’t have much time left to sleep, much less eat. In the end, but business of creating quality data just doesn’t get done, or doesn’t get done well. In the end, data quality is a business issue, supported by IT, but the business facing part of the solution has been missing.
But that is about to change. Informatica already provides the most scalable data quality product for handling the automated portion of the data quality process. And now, in the latest release of Informatica Data Quality 9.6, we have created a new edition called the Data Quality Governance Edition to fully manage the exception process. This edition provides a completely governed process for managing remediation of data exceptions by business data stewards. It allows organizations to create their own customized process with different levels of review. Additionally, it makes it possible for business users to create their own data quality rules, describing the rules in plain language…. no coding necessary.
And of course, every organization wants to be able to track how they are improving. And Informatica Data Quality 9.6 includes embeddable dashboards that show the progress of how data quality is improving and impacting the business in a positive way.
Great data isn’t an accident. Great data happens by design. And for the first time, data cleansing has been combined with a holistic data stewardship process, allowing business and IT to collaborate to create quality data that supports critical business processes.
Over the last 40 years, data has become increasingly distributed. It used to all sit on storage connected to a mainframe. It used to be that the application of computing power to solve business problems was limited by the availability of CPU, memory, network and disk. Those limitations are no longer big inhibitors. Data fragmentation is now the new inhibitor to business agility. Data is now generated from distributed data sources not just within a corporation, but from business partners, from device sensors and from consumers Facebook-ing and tweeting away on the internet.
So to solve any interesting business problem in today’s fragmented data world, you now have to pull data together from a wide variety of data sources. That means business agility 100% depends on data integration agility. But how do you do deliver that agility in a way that is not just fast, but reliable, and delivers high quality data?
First, to achieve data integration agility, you need to move from a traditional waterfall development process to an agile development process.
Second, if you need reliability, you have to think about how you start treating your data integration process as a critical business process. That means thinking about how you will make your integration processes highly available. It also means you need to monitor and validate your operational data integration processes on an ongoing basis. The good news is that the capabilities you need for data validation as well as operational monitoring and alerting for your data integration process are now built into Informatica’s newest PowerCenter Edition, PowerCenter Premium Edition.
Lastly, the days where you can just move data from A to B without including a data quality process are over. Great data doesn’t happen by accident, it happens by design. And that means you also have to build in data quality directly into your data integration process.
Great businesses depend on great data. And great data means data that is delivered on time, with confidence and with high quality. So think about how your understanding of data integration and great data can make your career. Great businesses depend on great data and people like you who have the skills to make a difference. As a data professional, the time has never been better for you to make a contribution to the greatness of your organization. You have the opportunity to make a difference and have an impact because your skills and your understanding of data integration has never been more critical.
Maybe the word “death” is a bit strong, so let’s say “demise” instead. Recently I read an article in the Harvard Business Review around how Big Data and Data Scientists will rule the world of the 21st century corporation and how they have to operate for maximum value. The thing I found rather disturbing was that it takes a PhD – probably a few of them – in a variety of math areas to give executives the necessary insight to make better decisions ranging from what product to develop next to who to sell it to and where.
Don’t get me wrong – this is mixed news for any enterprise software firm helping businesses locate, acquire, contextually link, understand and distribute high-quality data. The existence of such a high-value role validates product development but it also limits adoption. It is also great news that data has finally gathered the attention it deserves. But I am starting to ask myself why it always takes individuals with a “one-in-a-million” skill set to add value. What happened to the democratization of software? Why is the design starting point for enterprise software not always similar to B2C applications, like an iPhone app, i.e. simpler is better? Why is it always such a gradual “Cold War” evolution instead of a near-instant French Revolution?
Why do development environments for Big Data not accommodate limited or existing skills but always accommodate the most complex scenarios? Well, the answer could be that the first customers will be very large, very complex organizations with super complex problems, which they were unable to solve so far. If analytical apps have become a self-service proposition for business users, data integration should be as well. So why does access to a lot of fast moving and diverse data require scarce PIG or Cassandra developers to get the data into an analyzable shape and a PhD to query and interpret patterns?
I realize new technologies start with a foundation and as they spread supply will attempt to catch up to create an equilibrium. However, this is about a problem, which has existed for decades in many industries, such as the oil & gas, telecommunication, public and retail sector. Whenever I talk to architects and business leaders in these industries, they chuckle at “Big Data” and tell me “yes, we got that – and by the way, we have been dealing with this reality for a long time”. By now I would have expected that the skill (cost) side of turning data into a meaningful insight would have been driven down more significantly.
Informatica has made a tremendous push in this regard with its “Map Once, Deploy Anywhere” paradigm. I cannot wait to see what’s next – and I just saw something recently that got me very excited. Why you ask? Because at some point I would like to have at least a business-super user pummel terabytes of transaction and interaction data into an environment (Hadoop cluster, in memory DB…) and massage it so that his self-created dashboard gets him/her where (s)he needs to go. This should include concepts like; “where is the data I need for this insight?’, “what is missing and how do I get to that piece in the best way?”, “how do I want it to look to share it?” All that is required should be a semi-experienced knowledge of Excel and PowerPoint to get your hands on advanced Big Data analytics. Don’t you think? Do you believe that this role will disappear as quickly as it has surfaced?
I love exploring new places. I’ve had exceptional experiences at the W in Hong Kong, El Dorado Royale in the Riviera Maya and Ventana Inn in Big Sur. I belong to almost every loyalty program under the sun, but not all hospitality companies are capitalizing on the potential of my customer information. Imagine if employees had access to it so they could personalize their interactions with me and send me marketing offers that appeal to my interests.
Do I have high expectations? Yes. But so do many travelers. This puts pressure on marketing and sales executives who want to compete to win. According to Deloitte’s report, “Hospitality 2015: Game changers or spectators?,” hospitality companies need to adapt to meet consumers’ increasing expectations to know their preferences and tastes and to customize packages that suit individual needs.
In this interview, Jeff Klagenberg, senior principal at Myers-Holum, explains how one of the largest, most customer-focused companies in the hospitality industry is investing in better customer, product, and asset information. Why? To personalize customer interactions, bundle appealing promotion packages and personalize marketing offers across channels.
Q: What are the company’s goals?
A: The executive team at one of the world’s leading providers of family travel and leisure experiences is focused on achieving excellence in quality and guest services. They generate revenues from the sales of room nights at hotels, food and beverages, merchandise, admissions and vacation club properties. The executive team believes their future success depends on stronger execution based on better measurement and a better understanding of customers.
Q: What role does customer, product and asset information play in achieving these goals?
A: Without the highest quality business-critical data, how can employees continually improve customer interactions? How can they bundle appealing promotional packages or personalize marketing offers? How can they accurately measure the impact of sales and marketing efforts? The team recognized the powerful role of high quality information in their pursuit of excellence.
Q: What are they doing to improve the quality of this business-critical information?
A: To get the most value out of their data and deliver the highest quality information to business and analytical applications, they knew they needed to invest in an integrated information management infrastructure to support their data governance process. Now they use the Informatica Total Customer Relationship Solution, which combines data integration, data quality, and master data management (MDM). It pulls together fragmented customer information, product information, and asset information scattered across hundreds of applications in their global operations into one central, trusted location where it can be managed and shared with analytical and operational applications on an ongoing basis.
Q: How will this impact marketing and sales?
A: With clean, consistent and connected customer information, product information, and asset information in the company’s applications, they are optimizing marketing, sales and customer service processes. They get limitless insights into who their customers are and their valuable relationships, including households, corporate hierarchies and influencer networks. They see which products and services customers have purchased in the past, their preferences and tastes. High quality information enables the marketing and sales team to personalize customer interactions across touch points, bundle appealing promotional packages, and personalize marketing offers across channels. They have a better understanding of which marketing, advertising and promotional programs work and which don’t.
Q: What is the role did the marketing and sales leaders play in this initiative?
A: The marketing leaders and sales leaders played a key role in getting this initiative off the ground. With an integrated information management infrastructure in place, they’ll benefit from better integration between business-critical master data about customers, products and assets and transaction data.
Q. How will this help them gain customer insights from “Big Data”?
A. We helped the business leaders understand that getting customer insights from “Big Data” such as weblogs, call logs, social and mobile data requires a strong backbone of integrated business-critical data. By investing in a data-centric approach, they future-proofed their business. They are ready to incorporate any type of data they will want to analyze, such as interaction data. A key realization was there is no such thing as “Small Data.” The future is about getting very bit of understanding out of every data source.
Q: What advice do you have for hospitality industry executives?
A: Ask yourself, “Which of our strategic initiatives can be achieved with inaccurate, inconsistent and disconnected information?” Most executives know that the business-critical data in their applications, used by employees across the globe, is not the highest quality. But they are shocked to learn how much this is costing the company. My advice is talk to IT about the current state of your customer, product and asset information. Find out if it is holding you back from achieving your strategic initiatives.
Also, many business executives are excited about the prospect of analyzing “Big Data” to gain revenue-generating insights about customers. But the business-critical data about customers, products and assets is often in terrible shape. To use an analogy: look at a wheat field and imagine the bread it will yield. But don’t forget if you don’t separate the grain from the chaff you’ll be disappointed with the outcome. If you are working on a Big Data initiative, don’t forget to invest in the integrated information management infrastructure required to give you the clean, consistent and connected information you need to achieve great things.
Murphy’s First Law of Bad Data – If You Make A Small Change Without Involving Your Client – You Will Waste Heaps Of Money
I have not used my personal encounter with bad data management for over a year but a couple of weeks ago I was compelled to revive it. Why you ask? Well, a complete stranger started to receive one of my friend’s text messages – including mine – and it took days for him to detect it and a week later nobody at this North American wireless operator had been able to fix it. This coincided with a meeting I had with a European telco’s enterprise architecture team. There was no better way to illustrate to them how a customer reacts and the risk to their operations, when communication breaks down due to just one tiny thing changing – say, his address (or in the SMS case, some random SIM mapping – another type of address).
In my case, I moved about 250 miles within the United States a couple of years ago and this seemingly common experience triggered a plethora of communication screw ups across every merchant a residential household engages with frequently, e.g. your bank, your insurer, your wireless carrier, your average retail clothing store, etc.
For more than two full years after my move to a new state, the following things continued to pop up on a monthly basis due to my incorrect customer data:
- In case of my old satellite TV provider they got to me (correct person) but with a misspelled last name at my correct, new address.
- My bank put me in a bit of a pickle as they sent “important tax documentation”, which I did not want to open as my new tenants’ names (in the house I just vacated) was on the letter but with my new home’s address.
- My mortgage lender sends me a refinancing offer to my new address (right person & right address) but with my wife’s as well as my name completely butchered.
- My wife’s airline, where she enjoys the highest level of frequent flyer status, continually mails her offers duplicating her last name as her first name.
- A high-end furniture retailer sends two 100-page glossy catalogs probably costing $80 each to our address – one for me, one for her.
- A national health insurer sends “sensitive health information” (disclosed on envelope) to my new residence’s address but for the prior owner.
- My legacy operator turns on the wrong premium channels on half my set-top boxes.
- The same operator sends me a SMS the next day thanking me for switching to electronic billing as part of my move, which I did not sign up for, followed by payment notices (as I did not get my invoice in the mail). When I called this error out for the next three months by calling their contact center and indicating how much revenue I generate for them across all services, they counter with “sorry, we don’t have access to the wireless account data”, “you will see it change on the next bill cycle” and “you show as paper billing in our system today”.
Ignoring the potential for data privacy law suits, you start wondering how long you have to be a customer and how much money you need to spend with a merchant (and they need to waste) for them to take changes to your data more seriously. And this are not even merchants to whom I am brand new – these guys have known me and taken my money for years!
One thing I nearly forgot…these mailings all happened at least once a month on average, sometimes twice over 2 years. If I do some pigeon math here, I would have estimated the postage and production cost alone to run in the hundreds of dollars.
However, the most egregious trespass though belonged to my home owner’s insurance carrier (HOI), who was also my mortgage broker. They had a double whammy in store for me. First, I received a cancellation notice from the HOI for my old residence indicating they had cancelled my policy as the last payment was not received and that any claims will be denied as a consequence. Then, my new residence’s HOI advised they added my old home’s HOI to my account.
After wondering what I could have possibly done to trigger this, I called all four parties (not three as the mortgage firm did not share data with the insurance broker side – surprise, surprise) to find out what had happened.
It turns out that I had to explain and prove to all of them how one party’s data change during my move erroneously exposed me to liability. It felt like the old days, when seedy telco sales people needed only your name and phone number and associate it with some sort of promotion (back of a raffle card to win a new car), you never took part in, to switch your long distance carrier and present you with a $400 bill the coming month. Yes, that also happened to me…many years ago. Here again, the consumer had to do all the legwork when someone (not an automatic process!) switched some entry without any oversight or review triggering hours of wasted effort on their and my side.
We can argue all day long if these screw ups are due to bad processes or bad data, but in all reality, even processes are triggered from some sort of underlying event, which is something as mundane as a database field’s flag being updated when your last purchase puts you in a new marketing segment.
Now imagine you get married and you wife changes her name. With all these company internal (CRM, Billing, ERP), free public (property tax), commercial (credit bureaus, mailing lists) and social media data sources out there, you would think such everyday changes could get picked up quicker and automatically. If not automatically, then should there not be some sort of trigger to kick off a “governance” process; something along the lines of “email/call the customer if attribute X has changed” or “please log into your account and update your information – we heard you moved”. If American Express was able to detect ten years ago that someone purchased $500 worth of product with your credit card at a gas station or some lingerie website, known for fraudulent activity, why not your bank or insurer, who know even more about you? And yes, that happened to me as well.
Tell me about one of your “data-driven” horror scenarios?
I recently had a lengthy conversation with a business executive of a European telco. His biggest concern was to not only understand the motivations and related characteristics of consumers but to accomplish this insight much faster than before. Given available resources and current priorities this is something unattainable for many operators.
Unlike a few years ago – remember the time before iPad – his organization today is awash with data points from millions of devices, hundreds of device types and many applications.
One way for him to understand consumer motivation; and therefore intentions, is to get a better view of a user’s network and all related interactions and transactions. This includes his family household, friends and business network (also a type of household). The purpose of householding is to capture social and commercial relationships in a grouping of individuals (or businesses or both mixed together) in order to identify patterns (context), which can be exploited to better serve a customer a new individual product or bundle upsell, to push relevant apps, audio and video content.
Let’s add another layer of complexity by understanding not only who a subscriber is, who he knows and how often he interacts with these contacts and the services he has access to via one or more devices but also where he physically is at the moment he interacts. You may also combine this with customer service and (summarized) network performance data to understand who is high-value, high-overhead and/or high in customer experience. Most importantly, you will also be able to assess who will do what next and why.
Some of you may be thinking “Oh gosh, the next NSA program in the making”. Well, it may sound like it but the reality is that this data is out there today, available and interpretable if cleaned up, structured and linked and served in real time. Not only do data quality, ETL, analytical and master data systems provide the data backbone for this reality but process-based systems dealing with the systematic real-time engagement of consumers are the tool to make it actionable. If you add some sort of privacy rules using database or application-level masking technologies, most of us would feel more comfortable about this proposition.
This may feel like a massive project but as many things in IT life; it depends on how you scope it. I am a big fan of incremental mastering of increasingly more attributes of certain customer segments, business units, geographies, where lessons learnt can be replicated over and over to scale. Moreover, I am a big fan of figuring out what you are trying to achieve before even attempting to tackle it.
The beauty behind a “small” data backbone – more about “small data” in a future post – is that if a certain concept does not pan out in terms of effort or result, you have just wasted a small pile of cash instead of the $2 million for a complete throw-away. For example: if you initially decided that the central lynch pin in your household hub & spoke is the person, who owns the most contracts with you rather than the person who pays the bills every month or who has the largest average monthly bill, moving to an alternative perspective does not impact all services, all departments and all clients. Nevertheless, the role of each user in the network must be defined over time to achieve context, i.e. who is a contract signee, who is a payer, who is a user, who is an influencer, who is an employer, etc.
Why is this important to a business? It is because without the knowledge of who consumes, who pays for and who influences the purchase/change of a service/product, how can one create the right offers and target them to the right individual.
However, in order to make this initial call about household definition and scope or look at the options available and sensible, you have to look at social and cultural conventions, what you are trying to accomplish commercially and your current data set’s ability to achieve anything without a massive enrichment program. A couple of years ago, at a Middle Eastern operator, it was very clear that the local patriarchal society dictated that the center of this hub and spoke model was the oldest, non-retired male in the household, as all contracts down to children of cousins would typically run under his name. The goal was to capture extended family relationships more accurately and completely in order to create and sell new family-type bundles for greater market penetration and maximize usage given new bandwidth capacity.
As a parallel track aside from further rollout to other departments, customer segments and geos, you may also want to start thinking like another European operator I engaged a couple of years ago. They were trying to outsource some data validation and enrichment to their subscribers, which allowed for a more accurate and timely capture of changes, often life-style changes (moves, marriages, new job). The operator could then offer new bundles and roaming upsells. As a side effect, it also created a sense of empowerment and engagement in the client base.
I see bits and pieces of some of this being used when I switch on my home communication systems running broadband signal through my X-Box or set-top box into my TV using Netflix and Hulu and gaming. Moreover, a US cable operator actively promotes a “moving” package to help make sure you do not miss a single minute of entertainment when relocating.
Every time now I switch on my TV, I get content suggested to me. If telecommunication services would now be a bit more competitive in the US (an odd thing to say in every respect) and prices would come down to European levels, I would actually take advantage of the offer. And then there is the log-on pop up asking me to subscribe (or throubleshoot) a channel I have already subscribed to. Wonder who or what automated process switched that flag.
Ultimately, there cannot be a good customer experience without understanding customer intentions. I would love to hear stories from other practitioners on what they have seen in such respect
Do you know what year the first steam engine locomotive was invented? 1804. It traveled 9 miles in two hours. Now, you and I would be pretty upset of we boarded a train and it took 2 hours to go 9 miles. But, 200 years ago, this was a huge innovation and led to the invention of the modern day train and railway.
Tremendous Growth In Demand for Rail Travel Puts Pressure on Rail Infrastructure
Today, Britain is experiencing tremendous growth in demand for rail travel. One million more trains and 500 million more passengers travel by train than just 5 years ago. Over the next 30 years passenger demand for rail will more than double and freight demand is expected to go up by 140%. This puts tremendous pressure on the rail infrastructure.
Network Rail is in the modern-day rail business. Employees work day and night running, maintaining and updating Britain’s rail infrastructure, including millions of assets, such as 22,000 miles of track, 6,500 crossings, 43,000 bridges, viaducts and tunnels. Improving the rail network provides faster, more frequent and more reliable journeys between Britain’s towns and cities.
Network Rail is investing more in the rail infrastructure than in Victorian times. In the last six months, they spent about $25 million a day! In a recent news release, Patrick Bucher, group finance director said, “We continue to invest record amounts to deliver a bigger, better railway for passengers and businesses across Britain. We are also driving down the cost of running Britain’s railway to help make it more affordable in the years ahead.”
Employees Need to Trust Asset Information to Pinpoint and Fix Problems Quickly
To pinpoint and fix problems quickly, keep their operating costs low and maintain a strong safety record, Network Rail’s employees need to trust their mission-critical asset information, such as:
- What is the problem?
- Where is it?
- What equipment, tools and skills are needed to fix it?
- Who is closest to the problem that could fix it?
Difficult to Make Sense of Asset Information Scattered across Applications
Similar to many companies their size, Network Rail’s mission-critical asset information was scattered across many applications, which made it difficult for employees to make sense of asset information and the interaction between assets.
The asset information team recognized the limitations of employees depending on an application-centric view of their business. To operate more efficiently and effectively, they needed clean asset information, consistent asset information, and connected asset information.
Investing in Rail Infrastructure AND the Information Infrastructure to Support It
Network Rail now uses a combination of data integration, data quality, and master data management (MDM) to manage their mission-critical asset information in a central location on an ongoing basis, to:
- make sense of asset information,
- understand the relationships between assets, and
- track changes to asset information.
In a news release, Patrick Bossert Director of Network Rail’s Asset Information services business said, “With more accurate and reliable information about assets and their condition our team can make better business decisions, enable innovation in our asset management policy, planning and execution, and improve rail-system-wide investment decisions that benefit the rail industry as a whole.”
If you work for a company that revolves around mission-critical asset information, ask yourself these questions:
- Can our employees makes sense of our asset information?
- Can they easily see relationships between assets and how they interact?
- Can they see the history of changes to asset information over time?
Or are are they limited by an application-centric view of the business because asset information is scattered across in multiple systems?
Have a similar story about how you are managing your mission-critical asset information? Please share it in the comments below.
As I continue to counsel insurers about master data, they all agree immediately that it is something they need to get their hands around fast. If you ask participants in a workshop at any carrier; no matter if life, p&c, health or excess, they all raise their hands when I ask, “Do you have broadband bundle at home for internet, voice and TV as well as wireless voice and data?”, followed by “Would you want your company to be the insurance version of this?”
Now let me be clear; while communication service providers offer very sophisticated bundles, they are also still grappling with a comprehensive view of a client across all services (data, voice, text, residential, business, international, TV, mobile, etc.) each of their touch points (website, call center, local store). They are also miles away of including any sort of meaningful network data (jitter, dropped calls, failed call setups, etc.)
Similarly, my insurance investigations typically touch most of the frontline consumer (business and personal) contact points including agencies, marketing (incl. CEM & VOC) and the service center. On all these we typically see a significant lack of productivity given that policy, billing, payments and claims systems are service line specific, while supporting functions from developing leads and underwriting to claims adjucation often handle more than one type of claim.
This lack of performance is worsened even more by the fact that campaigns have sub-optimal campaign response and conversion rates. As touchpoint-enabling CRM applications also suffer from a lack of complete or consistent contact preference information, interactions may violate local privacy regulations. In addition, service centers may capture leads only to log them into a black box AS400 policy system to disappear.
Here again we often hear that the fix could just happen by scrubbing data before it goes into the data warehouse. However, the data typically does not sync back to the source systems so any interaction with a client via chat, phone or face-to-face will not have real time, accurate information to execute a flawless transaction.
On the insurance IT side we also see enormous overhead; from scrubbing every database from source via staging to the analytical reporting environment every month or quarter to one-off clean up projects for the next acquired book-of-business. For a mid-sized, regional carrier (ca. $6B net premiums written) we find an average of $13.1 million in annual benefits from a central customer hub. This figure results in a ROI of between 600-900% depending on requirement complexity, distribution model, IT infrastructure and service lines. This number includes some baseline revenue improvements, productivity gains and cost avoidance as well as reduction.
On the health insurance side, my clients have complained about regional data sources contributing incomplete (often driven by local process & law) and incorrect data (name, address, etc.) to untrusted reports from membership, claims and sales data warehouses. This makes budgeting of such items like medical advice lines staffed by nurses, sales compensation planning and even identifying high-risk members (now driven by the Affordable Care Act) a true mission impossible, which makes the life of the pricing teams challenging.
Over in the life insurers category, whole and universal life plans now encounter a situation where high value clients first faced lower than expected yields due to the low interest rate environment on top of front-loaded fees as well as the front loading of the cost of the term component. Now, as bonds are forecast to decrease in value in the near future, publicly traded carriers will likely be forced to sell bonds before maturity to make good on term life commitments and whole life minimum yield commitments to keep policies in force.
This means that insurers need a full profile of clients as they experience life changes like a move, loss of job, a promotion or birth. Such changes require the proper mitigation strategy, which can be employed to protect a baseline of coverage in order to maintain or improve the premium. This can range from splitting term from whole life to using managed investment portfolio yields to temporarily pad premium shortfalls.
Overall, without a true, timely and complete picture of a client and his/her personal and professional relationships over time and what strategies were presented, considered appealing and ultimately put in force, how will margins improve? Surely, social media data can help here but it should be a second step after mastering what is available in-house already. What are some of your experiences how carriers have tried to collect and use core customer data?
Recommendations and illustrations contained in this post are estimates only and are based entirely upon information provided by the prospective customer and on our observations. While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warrantee or representation of success, either express or implied, is made.
The challenge for supermarkets today is balancing the needs of the customer against their ability to serve those needs. How are supermarkets and food manufacturers preparing their business for e-readiness? What about more customer centricity?
Currently, brands are not particularly good at serving consistent product information across in-store and online environments, leading to lower conversions and poor customer satisfaction. This shortfall is also preventing these brands from moving forward and innovating with new technologies. As a result, Product Information Management (PIM) is becoming a significant focus in effective omnichannel initiatives.
Consider the large range of products that can be seen at the average grocery store. The sheer number of categories is staggering, before you even consider the quantity of items in each category. There’s little wonder of local brands are struggling to replicate this level of product data anywhere else but on their store shelves.
Furthermore, consider the various kinds of information supermarkets are expected to include. Then, add to this the kinds of information supermarkets could include in order to present a competitive advantage over and above the rest. Information types currently possible are: Ingredients, additives, Images and videos, marketing copy, gene manipulation information, references, product seals, allergens, nutritional facts, translations, product categories, expiration/use-by dates, variants, region-specific information, GSDN information and more.
Ultimately, supermarkets are already on the path of improving consumers’ shopping experience and a few of the emerging technologies indicate the way this industry will continue to evolve.
6 Examples of food retail and supermarket trends
The below six examples demonstrate an emerging trend in grocery shopping, while also highlighting the need for accurate product information creation, curation and distribution.
- Ready-to-cook product bundles: Nice and very customer facing concept is done by German food retailer www.kochhaus.de (meaning house of cooking). The only offer product bundles of all ingredients which are required to cook a certain meal for the required number of guests. It can be seen as the look books which are well established at fashion brands and retailers sales strategy.
- Self-checkout Systems – More supermarkets are beginning to include self-checkouts. American and UK companies lead, Germany or Australia are behind. But there is the same risk of cart abandonment here as there is online, so providing a comprehensive and rich suite of product information at these POS systems is crucial.
- In-store Information Kiosks – Some supermarkets are beginning to include interactive displays in-store, with some even providing tablets mounted onto shopping trolleys. These displays serve in place of an in-store sales assistant, providing consumers with directions, promotions and complete access to product information (such as stock levels) on any item in the store.
- Supermarket Pop-ups – Food retailers are increasingly experimenting and improving the traditional shopping experience. One example that has turned the bricks-and-mortar concept on its head is electronic shopping ‘walls’, where products are prominently displayed in a high-traffic area. Consumers are able to access product details and make purchases by scanning a code presented alongside the image of a given product.
- Store-to-door Delivery Services – It’s starting to become commonplace. Not only are supermarkets offering same-day delivery services, the major brands are also experimenting with click and collect services. These supermarkets are moving toward websites that are just as busy and provide as much, if not more relevant content as their bricks-and-mortar outlets.
- App Commerce: Companies, like German food retailer Edeka offer an app for push marketing, or help matching customer profiles of dietary or allergy profiles with QR-code scanned products on the shopping list within the supermarket app.
What is next?
The supermarket of the future:
Reviving Customer Loyalty with leveraging information potential
Due to the increased transparency brought on by the ‘Google Era’, retailers have experienced a marked decline in customer loyalty. This concept of omnichannel shopping behaviour has led previously loyal customers to shop elsewhere.
Putting customers in the centre of all retail activities may not be a new trend, but in order to achieve it, retailers must foster more intelligent touch points. The supermarkets of the future will combine both product and customer data in such a way that every touch point presents a uniquely personalised experience for the customer, and a single, 360-degree view of the customer to the retailer.
The major supermarket brands already have comprehensive customer loyalty programs and they’re building on these with added products, such as consumer insurance packages. However, these initiatives haven’t necessarily led to an increase in loyalty.
Instead, the imperative to create a personal, intimate connection with consumers will eventually lead to a return in loyalty. The supermarket of the future will be able to send recipe and shopping list recommendations directly to the shopper’s preferred device, taking into account any allergies or delivery preferences.
Gamification as a tool for loyalty?
Moreover, this evolution will slowly lead into another phase of loyalty marketing: gamification. Comprehensive and detailed product data will form the basis of a loyalty program that includes targets, goals and rewards for loyal customers. The more comprehensive and engaging these shopping ‘games’ become, the more successful they will be from a marketing and loyalty perspective. However, the demands for detailed, accurate product information will also increase accordingly.
Private side note: My wife likes the simple Edaka App Game, where users need to cut slices of sausages. The challenge you need to hit exactly the weight the customer requires, like the in-store associate.
Those supermarkets that can deploy these initiatives first – and continue to innovate beyond this point – will have a bright future. Those that lag behind when it comes to leveraging their information and real time process might quickly begin to fade away.
What can I cook of my fridge remains?
I have been working all week long on the next year planning, so my fridge was not feeded well this week. Being almost empty the asks are
- What products are left?
- When do they expire?
- What can I cook of my fridge leftovers? (receipts)
- Where do I get the missing items for dinner with my wife? – And for which price
- Do they all match with my dietary and here allergy to nuts?
- Can I order online?
- When will they get delivered?
- What things can make our evening a success? The right wine recommendation? Two candles?
Well it is up to your imagination which products also can be sold in addition to make the customer happy and create a nice candle light dinner… But at least a good reason to increase the assortment.