Category Archives: Retail
Recently, my US-based job led me to a South African hotel room, where I watched Germany play Brazil in the World Cup. The global nature of the event was familiar to me. My work covers countries like Malaysia, Thailand, Singapore, South Africa and Costa Rica. And as I pondered the stunning score (Germany won, 7 to 1), my mind was drawn to emerging markets. What defines an emerging market? In particular, what are the data-related themes common to emerging markets? Because I work with global clients in the banking, oil and gas, telecommunications, and retail industries, I have learned a great deal about this. As a result, I wanted to share my top 5 observations about data in Emerging Markets.
1) Communication Infrastructure Matters
Many of the emerging markets, particularly in Africa, jumped from one or two generations of telco infrastructure directly into 3G and fiber within a decade. However, this truth only applies to large, cosmopolitan areas. International diversification of fiber connectivity is only starting to take shape. (For example, in Southern Africa, BRICS terrestrial fiber is coming online soon.) What does this mean for data management? First, global connectivity influences domestic last mile fiber deployment to households and businesses. This, in turn, will create additional adoption of new devices. This adoption will create critical mass for higher productivity services, such as eCommerce. As web based transactions take off, better data management practices will follow. Secondly, European and South American data centers become viable legal and performance options for African organizations. This could be a game changer for software vendors dealing in cloud services for BI, CRM, HCM, BPM and ETL.
2) Competition in Telecommunication Matters
If you compare basic wireless and broadband bundle prices between the US, the UK and South Africa, for example, the lack of true competition makes further coverage upgrades, like 4G and higher broadband bandwidths, easy to digest for operators. These upgrades make telecommuting, constant social media engagement possible. Keeping prices low, like in the UK, is the flipside achieving the same result. The worst case is high prices and low bandwidth from the last mile to global nodes. This also creates low infrastructure investment and thus, fewer consumers online for fewer hours. This is often the case in geographically vast countries (Africa, Latin America) with vast rural areas. Here, data management is an afterthought for the most part. Data is intentionally kept in application silos as these are the value creators. Hand coding is pervasive to string data together to make small moves to enhance the view of a product, location, consumer or supplier.
3) A Nation’s Judicial System Matters
If you do business in nations with a long, often British judicial tradition, chances are investment will happen. If you have such a history but it is undermined by a parallel history of graft from the highest to the lowest levels because of the importance of tribal traditions, only natural resources will save your economy. Why does it matter if one of my regional markets is “linked up” but shipping logistics are burdened by this excess cost and delay? The impact on data management is a lack of use cases supporting an enterprise-wide strategy across all territories. Why invest if profits are unpredictable or too meager? This is why small Zambia or Botswana are ahead of the largest African economy, Nigeria.
4) Expertise Location Matters
Anybody can have the most advanced vision on a data-driven, event-based architecture supporting the fanciest data movement and persistence standards. Without the skill to make the case to the business it is a lost cause unless your local culture still has IT in charge of specifying requirements, running the evaluation, selecting and implementing a new technology. It is also done for if there are no leaders who have experienced how other leading firms in the same or different sector went about it (un)successfully. Lastly, if you don’t pay for skill, your project failure risk just tripled. Duh!
5) Denial is Universal
No matter if you are an Asian oil company, a regional North American bank, a Central American National Bank or an African retail conglomerate. If finance or IT invested in any technologies prior and they saw a lack of adoption, for whatever reason, they will deny data management challenges despite other departments complaining. Moreover, if system integrators or internal client staff (mis)understand data management as fixing processes (which it is not) instead of supporting transactional integrity (which it is), clients are on the wrong track. Here, data management undeservedly becomes a philosophical battleground.
This is definitely not a complete list or super-thorough analysis but I think it covers the most crucial observations from my engagements. I would love to hear about your findings in emerging markets.
Stay tuned for part 2 of this series where I will talk about the denial and embrace of corporate data challenges as it pertains to an organization’s location.
In my last blog, I talked about the dreadful experience of cleaning raw data by hand as a former analyst a few years back. Well, the truth is, I was not alone. At a recent data mining Meetup event in San Francisco bay area, I asked a few analysts: “How much time do you spend on cleaning your data at work?” “More than 80% of my time” and “most my days” said the analysts, and “they are not fun”.
But check this out: There are over a dozen Meetup groups focused on data science and data mining here in the bay area I live. Those groups put on events multiple times a month, with topics often around hot, emerging technologies such as machine learning, graph analysis, real-time analytics, new algorithm on analyzing social media data, and of course, anything Big Data. Cools BI tools, new programming models and algorithms for better analysis are a big draw to data practitioners these days.
That got me thinking… if what analysts said to me is true, i.e., they spent 80% of their time on data prepping and 1/4 of that time analyzing the data and visualizing the results, which BTW, “is actually fun”, quoting a data analyst, then why are they drawn to the events focused on discussing the tools that can only help them 20% of the time? Why wouldn’t they want to explore technologies that can help address the dreadful 80% of the data scrubbing task they complain about?
Having been there myself, I thought perhaps a little self-reflection would help answer the question.
As a student of math, I love data and am fascinated about good stories I can discover from them. My two-year math program in graduate school was primarily focused on learning how to build fabulous math models to simulate the real events, and use those formula to predict the future, or look for meaningful patterns.
I used BI and statistical analysis tools while at school, and continued to use them at work after I graduated. Those software were great in that they helped me get to the results and see what’s in my data, and I can develop conclusions and make recommendations based on those insights for my clients. Without BI and visualization tools, I would not have delivered any results.
That was fun and glamorous part of my job as an analyst, but when I was not creating nice charts and presentations to tell the stories in my data, I was spending time, great amount of time, sometimes up to the wee hours cleaning and verifying my data, I was convinced that was part of my job and I just had to suck it up.
It was only a few months ago that I stumbled upon data quality software – it happened when I joined Informatica. At first I thought they were talking to the wrong person when they started pitching me data quality solutions.
Turns out, the concept of data quality automation is a highly relevant and extremely intuitive subject to me, and for anyone who is dealing with data on the regular basis. Data quality software offers an automated process for data cleansing and is much faster and delivers more accurate results than manual process. To put that in math context, if a data quality tool can reduce the data cleansing effort from 80% to 40% (btw, this is hardly a random number, some of our customers have reported much better results), that means analysts can now free up 40% of their time from scrubbing data, and use that times to do the things they like – playing with data in BI tools, building new models or running more scenarios, producing different views of the data and discovering things they may not be able to before, and do all of that with clean, trusted data. No more bored to death experience, what they are left with are improved productivity, more accurate and consistent results, compelling stories about data, and most important, they can focus on doing the things they like! Not too shabby right?
I am excited about trying out the data quality tools we have here at Informtica, my fellow analysts, you should start looking into them also. And I will check back in soon with more stories to share..
The World Cup drives a profound number of purchases. These purchases expose a stunning amount of what I call “PIM malpractice.” When there is a sudden surge in online product comparisons, the companies with effective Product Information Management benefit the most. The companies that lack effective PIM lose revenue.
It should be a no-brainer for electronics retailers to make sure their TV category product information is complete and up-to-date. After all, attributes sell. Unfortunately, many retailers still don’t understand the importance of consistent, accurate product information. Product information does sell – especially online, where shoppers go for product research. This is especially true for a spec-heavy tech purchase like a big-screen TV.
Product information has enormous power. When it is accurate and consistent, it has the power to excite and guide shoppers. When it is incomplete or incorrect, product information can create deal-breaking insecurity.
A case in point:
Let’s for a minute replicate the customer journey to that new TV set. Say you want a new HD TV with a 50-inch screen. Your budget is around $1,200, and your spouse said “yes” under the condition that it’s wall-mountable. You visit an online shop and filter your search by price and screen size. The result: no fewer than 25 models to compare. This is what a detailed view of one of them looks like:
Apart from the prioritization of essential information (shouldn’t the screen size be displayed above the model number?), it seems pretty impressive. There are lot of information and explanations (hidden behind the information icons) that make the product take shape in your mind and help you learn what to look for. The only thing that seems to be missing is information about the wall mount…
What we can learn from the comparison function
Once you look at a few products in comparison, however, the situation changes quite a bit. Say you choose four TVs from your filtered search results and hit “compare products.” This is what your screen looks like now:
The comparison view reveals the kind of product information deficiencies that inhibit purchase. Here’s what we can learn from it:
- Data on all four products was incomplete. One of the first things the customer sees is a whole lot of grey: product information that’s missing. The single product view only gave the available attributes but the comparison function highlights the gaps. And gaps are bad, because…
- The product with the most attributes sets the standard. Customers look to product information to tell them what they should know. Missing information or an attribute that isn’t defined always look careless – and what’s worse, it makes the product look inferior: If they haven’t bothered, they can’t think too much of the product, right?
- Product information doesn’t simply exist, waiting to be written down. You have to create it. When it’s designed well, it sets standards and helps your products rise above the competition.
- The unanswered question is always the most prominent. You still don’t know about the wall mount… And as a matter of fact, whatever far-fetched detail customers may want to know – it will always be the first thing on their mind. It may be their dream TV, but unless they know that one thing, they just can’t buy it.
So when I said in the beginning that brands and retailers don’t understand that product information sells, this is what I meant:
Modern shoppers always research product information, especially when making a major purchase such as TV set. That product information isn’t neutral, or nice-to-have. It is the product. And it needs to be treated with the same care as the product itself.
Retailers need to create their own standards: It’s not enough to just display supplier information. Rigorous quality control and information completion processes need to be in place if retailers want their product information to be better than the competition’s.
Great PIM comes from the customer’s point of view. When designing product information, the customer is the ultimate guide. Immersing oneself in their situation, and investing time and the combined brain power of category experts to think about anything they may want to know – it may the make or break of a sale.
That’s one recent example in one product category. But the PIM problem can be seen across every retail category and on virtually every retail website or mobile app.
To brands and retailers that get it right, multichannel product information management is a secret advantage. It’s also the best way to leap out of the product comparison tables and into the shopping carts.
Imagine the wall mount is there and helps to convince the mainly male target group. Which information is needed to tailor digital marketing to different personas and target groups, depending on teams, nations, locations or what information can help to personalize marketing to people which are maybe not keen on football. What would attract them?
What do you think? Did you find the right large-screen TV for World Cup watching? The World Cup shows everyday PIM malpractice. We’d love to hear about your most sought-after but least-found television attribute! (And for more on the importance of product information, check out our ebook “The Informed Purchase Journey.”)
Did you know the 2014 Brasil World Cup is actually the World Cup of Data? In addition to the visible matches played on the pitch, eShops will be in a simultaneous struggle to win real-time online merchandise customers.
Let me explain. Jogi Löw, the manager of the German team, is known for his stylish attire. At every major event, each European Cup and World Cup, he wears newly designed shirts and suits. As a result, when television audiences see each new article of clothing, there is a corresponding increase in related online retail activity. When Löw began this tradition, people didn’t know that his outfits were made by Strenesse. As a result, people searched using the keywords “Jogi Löw Shirt.” This drove traffic to the eShop with the best search engine optimization, giving them more conversions and more revenue.
If a manager’s attire drives online retail sales, imagine how much demand there is for the jerseys worn by the most visible World Cup athletes? Many of the these players have huge social media followings. Consider the size of the social media followings of Ronaldo, Kakà, Neymar, Ronaldinho and Wayne Rooney:
There is huge demand for these player’s jerseys. This demand will only increase as the games progress. Once the winner is decided, Google searches will rise for phrases like “World Cup Winner Jersey 2014 of xxx”. Some refer to this as the super long tail. And research does show that search queries with 3 or more words have better conversion rates than queries with only 1 or 2 words.
Who can predict the winners?
What happens if a fairly unknown player scores the last goal in over time? How will that event impact social media activity and search engine volumes? Who will be able to leverage this activity to sell the relevant merchandising products fast enough? The eShop with the best data will have the quickest response. And the eShop with the quickest response will get the traffic and the revenue.
The world cup is a battle. The early bird closes the sale. It’s time to play the World Cup of Data.
In my marketing classes, I like to share on the works of Michael Porter’s Competitive Strategy. This includes discussing his three generic business strategies. We discuss, for example, the difference between an “efficiency strategy” (aka Walmart) and an “effectiveness strategy” (aka Target or even better, a high end service oriented retailer). I always make sure that students include in their thinking on differentiation the impact of customer service.
One of these high end service oriented retailers is using technology to increase its customer intimacy as well as holistic customer knowledge. Driving this for them involves understanding when customers use their full price and off price customer purchase channels. I was so fascinated about their question that I decided to ask the font of all wisdom, my wife. She said that her choice of channel is based on my current salary or her projected length of use of an item. So if she is buying a jacket that she wants to use for years, she will go to the full price channel but for a dress or pair of shoes for one time use like a Wedding, she will go to the lower priced channel. Clearly, there is more than one answer to these questions. This retailer wants to understand the answers by customer segments.
To create an understanding of each customer segment, this retailer wants to create a “high fidelity” view of data coming from customers, markets, and transactional interactions. This means that that they need two new business capabilities. First is a single integrated view of their customers across channels and the ability to see the cause and effect of customer channel selection decisions. Do customers spend more time at the full price channel option when, for example, sale offerings are going on?
To solve these problems, the retailer has implemented two technology approaches, master data management to bring together its disparate views of customer and big data for quick hypothesis testing of customer data from structured and unstructured sources. With Master Data, they get a single view of customer across differing IT systems. For separate customer specific analysis they have created operational and analytic views on top of the MDM system. And while they have an enterprise data warehouse and multiple analytical data marts, they have also created a HADOOP cluster to test hypothesis about the cross channel customer segments. They are using the single view of customer regardless of channels and transaction history to understand when customers use which channel and as well what marketing or other campaigns pulled the customer in. With this, they are creating inferred attributes for customer market segments.
Clearly, the smarter the retailer gets, the greater the differentiation the retailer services can be to customers. At the same time, the data let’s the retailer optimize marketing between channels. This is using data to create service differentiation.
For years, a customer’s purchase process was something of “An Unexpected Journey.” Lack of insight into the journey was a struggle for retailers. The journey was fraught with questions about product research habits, purchases and crucial factors that spark purchase decisions.
Today, the customer purchase journey no longer has to be a “guessing game.” Data integration and analytics are able to assist retailers in understanding this journey. To begin, let’s examine how consumer behaviors and the role of product information have changed since the advent of substantial bandwidths and social buying. To do so, lets examine the way shoppers buy today.
The customer buying experience has changed in the following ways:
The days of the single visit to a trusted retailer are behind us. Today’s shoppers are in control. They are hugely aware of their power as consumers, and they’re exercising it freely.
Buyers aren’t using one specific channel anymore. They’re shopping in stores, online, through mobile apps, on social platforms, and from catalogs simultaneously. Lacking a central focal point, quality data integration and analytics have become imperative to understanding this behavior. Retailers must be able to track the purchase decisions of one consumer as he or she switches back and forth amongst these channels. If done correctly, a retailer would be able to recognize behavior specific to individuals and act on it, serving ads or timely discounts to them.
Purchasing decisions are “crowd-informed.” Recommendations and reviews from peers guide consumers and validate their choices every step of the way. As a result, it has become increasingly necessary for retailers to understand how they are being reviewed. But more specifically, it is important for the retailer to identify and target influential reviewers. If this is done effectively, the retailer may be able to personalize their experience and make that influential consumer feel special. This may seem like a complicated task with small returns, but imagine if they write a positive review that is ultimately read by thousands of people. This could lead to a fantastic return on investment for the retailer.
Shoppers used to be dependent on a few sources of information. Now with Internet search tools, consumers are able to hunt for answers themselves. As such, retailers must understand what type of information their consumers are searching for. With this information, retailers may be able to update the content on their websites, blogs, or social channels to provide information customers need. To visualize this purchase journey we’ve created the INFAgraphic below.
So how can I learn more?
Join us at Informatica World 2014 to learn rich information about retail technology and the “purchase journey.”
Experts will share ways of leveraging your data to boost sales and heighten customer experience. The conference also has a dedicated MDM Day on Monday May 12 with workshops and sessions showing how vendors, distributors, retailers and individuals interact in the “always-on,” connected world.
Reserve your spot by signing up here.
Over the past few years, we have assisted an increasing shift in customer behavior. Pervasive internet connectivity – along with the exponential adoption of mobile devices – has enabled shoppers to research and purchase products of all kinds, anytime and anywhere, using a combination of touch points they find most convenient. This is not a passing fad.
Consumers expect rich data and images to make purchase choices; business users require access to analytical data in order to make mission-critical decisions. These demands for information are driving a need for improved product data availability and accuracy. And this is changing the way businesses go to market.
A staggering number of stores and manufacturers are reforming their models to response to this challenge. The direct-to-consumer (DTC) model, while not new, is rapidly becoming the center stage to address these challenges. The optimal DTC model will vary depending on specific and contextual business objectives. However, there are many strategic benefits to going direct, but the main objectives include growing sales, gaining control over pricing, strengthening the brand, getting closer to consumers, and testing out new products and markets.
It is my contention that while the DTC model is gaining the deserved attention, much remains to be done. In fact, among many challenges that DTC poses, the processes and activities associated with sourcing product information, enriching product data to drive sales and lower returns, and managing product assortments across all channels loom large. More precisely, the challenges that need to be overcome are better exemplified by these points:
- Products have several variations to support different segments, markets, and campaigns.
- Product components, ingredients, care information, environmental impact data and other facets of importance to the customer.
- People are visual. As a result, easy website navigation is essential. Eye-catching images that highlight your products or services (perhaps as they’re being performed or displayed as intended) is an effective way to visually communicate information to your customers and make it easier for them to evaluate options. If information and pictures are readily accessible, customers are more likely to engage.
- Ratings, reviews and social data, stored within the product’s record rather than in separate systems.
- Purchasing and sales measurements, for example, sales in-store, return rates, sales velocity, product views online, as well as viewing and purchasing correlations are often held across several systems. However, this information is increasingly needed for search and recommendation.
The importance of product data and its use, combined with the increased demands on business as a result of inefficient, non-scaling approaches to data management, provide an imperative to considering a PIM to ‘power’ cross-channel retail. Once established, PIM users repeatedly report higher ROI. It is likely that we’ll see PIM systems rank alongside CRM, ERP, CMS, order management and merchandising systems as the pillars of cross-channel retailing at scale.
For all these reasons, choosing the right PIM strategy (and partner) is now a key decision. Get this decision wrong and it could become an expensive mistake.
Recently, I ordered a pair of athletic pants from a high-fashion, online retailer. The pants were a well-known brand and cost $96.00. The package arrived within a few days. However, when I opened the box, I found it did not contain the product I expected. The brand and color were correct, but it was not the style I’d chosen. Disappointed, I wrote the retailer, explaining the issue and requesting the correct product. Then, I returned the incorrect product.
According to recent research, the average vendor’s “cost per return” is $20.00. That means that my return was a Margin Killer for the retailer.
Three days later, the replacement delivery arrived. Whoop there it is… Disappointment number two. It was the exact same incorrect product. Yet another Margin Killer, Return Number 2. Another $20.00 in costs for the retailer. What would it take for this retailer’s logistic team to avoid repeating their error? Could they scan the product? Could they use a QR code, a bar-code or some sort of picture?
I returned the incorrect product for the second time. Eventually, shipment number three reached my home. Can you guess what was in the box? Yes, the same incorrect product, again, for the third time. The Margin Killer: Return Number 3. For this retailer, the math is simple:
Return 1: $20.00
Return 2: $20.00
Return 3: $20.00
Total return cost: $60.00
Revenue = Possibly zero?
Funky side note: When browsing stores downtown on Saturday, I found the correct pants in a SportScheck store, and for ten dollars less! So remember, the modern customer is demanding, always-connected and shopping on an “Informed Purchase Journey”.
So how can I learn more?
If you work in retail technology, you will find rich information about this purchase journey at the Informatica World 2014 conference. The Retail Path track will feature insights from companies like Nike, Avent, Discount Tire, Nordstrom, Geiger, Intricity and Deloitte. Experts will share ways to leverage your data to boost your sales and heighten customer experience. The conference even has a dedicated MDM Day on Monday May 12 with workshops and sessions showing how vendors, distributors, retailers and individuals interact in the “always-on” connected world. Make sure you have a spot by signing up HERE.
Today, it is not uncommon for retailers to have multiple brands and several channels through which they sell their products. Due to changes in social behavior, consumers are demanding retailers provide relevant and interactive experiences at every touch point.
The Retail Path at Informatica World 14 is your chance to engage with the world’s leading retail brands and industry experts. The retail path covers topics such as expanding product assortment, introducing new products and reducing supply chain costs. It will focus on driving customer loyalty using social, local, mobile and customer feedback. You will learn about unique customer experiences with relevant information, analytics and relationships between data and people.
These sessions are great for leaders in ecommerce, marketing, supply chain retail and product management. They’ll be held during the MDM pre-conference (May 12) and Informatica World Retail Path (May 13-15).
SNEAK PREVIEW OF RETAIL PATH SESSIONS, MAY 13th
NIKE: Creative Uses of Informatica Data Quality and Data Services Nike has pushed the limits of Informatica Data Quality and Data Services by re-imagining uses for reference tables, scorecards with associated metadata, web services, and more. Learn how Nike adds value to its business customers and increases ROI by adding virtualization through data services. Speakers: Teresa Mains, Corbett Oliver, Udaya Vepakomma
Point of Sale: Retrieving your POS data in Near Real TimRetailers know that capturing Point of Sale (POS) data in a timely manner can drive customer loyalty and merchandising efficiencies. In this session, Intricity shares how the powerful Informatica Platform has enabled it to capture POS data in near real time — enabling its retailers to share brick and mortar inventory with web stores, drive intraday targeted marketing, and provide customers with the convenience of in-store pickups. Speaker: Arkady Kleyner
Nordstrom: Customer Service at its best – How Information Powers Nordstrom’s Customer Centricity Strategy
Known for its quality products and customer service, Nordstrom never stops innovating. Learn how Nordstrom uses “personal book” to drive revenue through customer personalization. Speakers: Vaidyanathan Seshan; Gopinath Raghavan
Avnet: Using Informatica B2B Data Exchange and B2B Data Transformation to Expand Your Trading Partner Portfolio
Business to Business (B2B) transaction automation is pursued by companies of all sizes due to the efficiencies and message integrity inherent to transactional automation. This session will explain how Avnet’s use of Informatica B2B Data Exchange and Data Transformation empowered its business to establish B2B integrations with small and medium size trading partners. Speakers: David Crowell, Anthony Daniel
SNEAK PREVIEW OF PRE-CONFERENCE MDM DAY, MAY 12th
Keynote: Deloitte’s Digital Influence - The New Digital Divide
The growing gap between the needs and expectations of shoppers and the digital experience brands and retailers are offering them.
Speaker: Jeff Simpson, Director, Deloitte
Best Practice: Transforming your business for tomorrow’s commerce – best practice with product information management
Geiger is the largest privately held promotional products distributor in the world and is the only distributor ranked in the “Top 10” for the last 30 straight years. In this energetic information-packed session, “The Selling CIO”, Dale Denham will talk about the role of product information in ecommerce and how it improved Geiger’s ability to promote and sell promotional products. Attendees will learn how to achieve business goals by identifying the critical steps involved in implementing a Product Information Management (PIM) system. Additionally, this session will cover how to use data and technology to support agility in sales and marketing operations.
Speaker: Dale Denham, CIO, Geiger
Panel discussion with sponsoring partners and speaking customers:
The global digital revolution: How vendors, distributors, retailers and individuals interact in the always on and connected world.
Moderator: Ben Rund, Sr. Director Product Marketing PIM & Procurement, Informatica
Innovations Connecting Buyers and Suppliers – What’s new in PIM and Procurement, Roadmap
Speakers: Stefan Reinhardt, Product Manager PIM, Jakki Geiger, Sr. Director Product Marketing MDM
Holistic Data Governance: A Framework for Competitive Advantage
Speaker: Rob Karel, VP Marketing & Strategy MDM
Four high value workshops, presented by domain experts and industry specialists:
- Workshop “Future of commerce use cases”: How recommendation, targeting, ecommerce, social and mobile need to leverage product information. Moderators: Nagesh Kanumury, Principle Product Manager & Rich Dase, Ideosity
- Workshop “Business use cases of collaboration and Business Processes Management”: Product information and beyond. Moderators: Daniel Walter, Product Manager PIM & Nimish Mehta, LumenData
- Workshop “Why business users required quality data”: use cases, rules, roles, dashboards and important KPIs. Moderators: Stefan Reinhardt, Product Manager PIM & Matt Wienke, Infoverity
- Workshop “Connecting the dots”: Business use cases leveraging the relations of different master data. Commerce Relevancy: Customer segmentation and product personalization. Supplier spend management and supplier catalogs. Moderators: Markus Schuster, Sr. Director Product Management PIM & Procurement & Naveen Sharma, Cognizant
Save your chair at this high value pre-conference day by signing up HERE.
Please feel free to contact me (firstname.lastname@example.org, +1 650 385 5151) or Cathy Wright (email@example.com, +1 650 385 5151) if you have any questions, or if you would like us to consider additional topics for the agenda. We look forward to seeing you at the MDM Day meeting on 12th May in Las Vegas.