Big Data Archives - Page 3 of 3 - Omnichannel Personalization SaaS Solution

20 Sep
2013

The Future: Big Opportunities in Big Data

Big Opportunities in Big Data Abound. You Just Need to Know HOW to Look (4th in a Series)

I recently spoke with several high level marketing executives about the near-ubiquitous topic, BIG DATA. The executives included Paul Golden, ex-CMO of Samsung Mobile, Barry Judge (ex-CMO of Best Buy, current CMO of LivingSocial, and Brad Todd, (Principal at The Richards Group). In this fourth installment, we will share these executives’ thoughts about the way big data can further revolutionize the future of marketing. They see big opportunities in Big Data. The best is yet to come!

When we posed the question to our marketing executives about the future of big data in the marketing realm, they were excited about the possibilities and big opportunities in big data’s future.

Paul Golden would like to see big data used to truly personalize the customer experience. Paul recalled a men’s retailer whose sales people would write your information, preferences and measurements on index cards (similar to today’s “preference centers”). They would use this information to follow up with their customers whenever an item arrived in-store that might fit a customer’s needs. In this age of big data, this type of information is available for so many customers—retailers could be delivering personalization at its best. Why don’t they create big opportunities with big data they have? With SO MUCH data, many executives just aren’t quite sure what to do with all that data, much less how to turn it into value for their brand and customers. Too much leads to not enough.

Creating a truly omnichannel experience, merging all big data from physical and digital channels would be the holy grail, according to Barry Judge. If you could combine store purchase data with online transactions and click behavior, plus email, mobile and social, throw in CRM and loyalty data, you’re well on your way to creating big opportunities in big data. The key is integrating all that big data into something that allows you to speak to each of your customers as an individual, regardless of where she interacts with your brand.

Brad Todd would like to see retailers, like grocery stores, who have been capturing data for decades, turn that knowledge into a one-to-one customer experience. It would be great, for instance, if the grocer “knew” that you shop weekly for strawberries but didn’t buy any this week; they could offer a real-time discount for you to pick up some strawberries during this shopping trip as well. Another one of the big opportunities in big data.

Today tools exist to make one-to-one personalization—hyper-personalization—a reality. The tough part is corralling all that big data and applying robust analytics to arrive at hyper-personalized communications for each customer. Want to learn how to overcome the tough part?

And please feel free to leave any comments or questions below.

19 Sep
2013

Big Data Analytics: The Key to Unlocking Business Potential

Positive Results From the Smart Use of Big Data Analytics (3rd in a Series)

I recently spoke with several high level marketing executives about the near-ubiquitous topic, BIG DATA. The executives included Paul Golden, ex-CMO of Samsung Mobile, Barry Judge (ex-CMO of Best Buy, current CMO of LivingSocial, and Brad Todd, (Principal at The Richards Group). In this third installment, we review some of the results these executives experienced. Big data analytics was the key in making the information they had actionable to drive customer value.

Brad Todd has helped clients use their data in very sophisticated ways, by applying rigorous big data analytics. A home improvement retailer, for instance, has used information from their customers’ do-it-yourself projects to engage in helpful conversations with their customers. This type of engagement not only makes the customer feel valued, but very often leads to follow-on projects and increased customer loyalty. For instance, if a customer has planned a deck using online tools, the retailer can follow up with them at predicted intervals with suggestions and relevant offers to improve the likelihood of purchase.

The Richards Group also helps their clients integrate their marketing data and then apply big data analytics, with the objective of personalizing customer communications. They have seen improvements of 20% on average when website, email and remarketing channels are personalized to customers. The results are even greater—about 25% if cross-channel personalization occurs.

At Samsung Mobile, Paul Golden used longitudinal brand preference data to prioritize markets for their marketing efforts. He and his team tailored brand messages and tactics for eight key markets to improve brand preference versus a key competitor. The result was a swing from a relative score of -6 to +2 in overall brand preference, despite only focusing on eight key markets. Big data analytics allowed Samsung Mobile to cost-effectively determine which markets would swing the entire country’s brand preference score in their favor.

While CMO of Best Buy, Barry Judge and his team applied big data analytics to vast amounts of customer information to zero in on their highest value customers. They then tailored all their marketing to best serve those customers and increase their engagement. Knowing their customers and what their shopping habits allowed Best Buy to offer the most relevant products and offers to promote via email and direct mail. By focusing on their most loyal customers, they grew their loyalty even more and increased their share of wallet with these customers.

Big data can be a big deal in driving results for brands if used to improve customer interactions. Set objectives, determine what data is needed to achieve those objectives, compile and analyze the data, then translate it into something valuable for your customers.

Want to learn more about how to use big data analytics to improve business results? Click here.

And please feel free to leave any comments or questions below.

05 Sep
2013

The Opportunities of BIG DATA: What Do You Think?

What Do You Think When You Hear the Term Big Data?

Opportunities of Big Data Lie beyond the Hyperbole (1st in a Series)

I recently spoke with several high level marketing executives about the near-ubiquitous topic, BIG DATA. The executives included Paul Golden, ex-CMO of Samsung Mobile, Barry Judge (ex-CMO of Best Buy, current CMO of LivingSocial, and Brad Todd, (Principal at The Richards Group). Generally, I wanted to get their points of view on the opportunities of big data.

I also wanted to get a better understanding of their thinking about:
1) how they perceive big data
2) how their companies use it
3) what kind of results they’ve experienced when leveraging that data, and
4) future opportunities of big data.

But first, before I got into the meat of the discussion, I asked each of them the same question: What do you think when you hear the term BIG DATA?

And here were their responses, in no particular order: cliche, digital, lots of customers/lots of interactions, complicated, limited actionability, hyperbole, blanket term. If one were to look at this list, one might draw the conclusion that BIG DATA has a BAD RAP.

But when we began to speak about the promise of big data, these same executives were much more positive and even excited about the opportunities of big data–the potential customer value it could deliver. The goal of big data is understandable and very desirable, but the steps to get to there are difficult to envision. Especially with all the hype today about big data, which often is just that–hype, a certain amount of cynicism has crept into the C-suite.

But today, you CAN turn all that big data into actionable information to deliver value to your customers by hyper-personalizing their experiences. From connecting all your data dots, to generating the most relevant customer messages, to omnichannel marketing communications, Nectar has the comprehensive marketing suite that can take you from A to Z, quickly and easily. Want to learn more about the opportunities of big data?

Stay tuned to the rest of our series as we find out what these executives think about actual use of big data in business.

Please feel free to leave a comment or ask a question in the section below.

29 Aug
2013

5 Tips for Building an Omni-Channel Customer Profile

Creating an Omni-Channel Customer Profile can be Easy, if you Start with the End in Mind

With all the clutter of marketing messages, customers are demanding relevance. At the same time, marketing teams are struggling with some of the basic foundational components because of all the disparate sources of data available both internally and externally { there I stayed away from saying big data } …The ability to communicate with your customers in an individual manner is becoming table stakes in both online and offline marketing, what we at Nectar Online Media like to call Hyper-Personalization. Whether you use the term 360-degree customer profile or omni-channel customer profile, the goal of creating a unified picture of your customer’s data is foundational for accurate customer analytics and also hyper-personalizing your interactions with your customers.

In this post, we thought we’d provide some of our tips for how to build an omni-channel customer profile. If you start with the end in mind (i.e., your marketing or business objective), it will be a lot easier.

# 1 Know Your Goal — It sounds simple and we’ve heard the same tip for many other areas, both in business and personal life. As it relates to customer analytics and hyper-personalization, the goal is based on how you want to use the customer data and, therefore, impacts the data sets you really need vs ideally want to have. By selecting the right data sets for building your omni-channel customer profile, your internal business partners and external providers can be much more focused (and efficient).

For example, Nectar works with an online ecommerce retailer, hipcycle.com, to help personalize their digital communications { if you’ve not checked out Hipcycle before, I strongly encourage you — you won’t be disappointed }.

Based on understanding Hipcycle’s marketing business objectives, we were able to hone in on the right data sets to integrate. These data sets were primarily based on transaction, crm, and behavior on hipcycle.com. While data sets like social media and household data provide an interesting lens, these data sets were not going to add incremental benefit & results that outweighed the effort.

# 2 Marketing & IT Need to Collaborate — While the marketing team can help define business objectives and outcomes based on using the omni-channel customer profile, the marketer’s technology counterparts are pivotal in articulating in identifying road blocks ahead of time and developing the right data streams.

If the marketing group is defining the customer analytics and hyper-personalization needs, involve the technology teams early on in the process to be better informed on constraints, timelines, and the ‘art of the possible.’

# 3 Choose the Right Technology — Different technologies are appropriate for different business objectives. If you are aiming to build an omni-channel customer profile, our experience has found a traditional SQL (row & records) environment is not optimal. Why? In a nutshell, because of all the different data sources and likely millions of records, there is a fair amount of processing a system needs to do before you can see the results (analysis, reports, recommendations, etc.) that you are looking.

At Nectar Online, we’ve found a noSQL environment is much better suited for storing data records for the purpose of utilizing that 360-degree view of the customer. The primary benefit is that data is stored in an array … so at the instance when data needs to be processed for an individual customer, information is ready.

# 4 Relevant Refreshes — An important component to evaluate is the frequency of your omni-channel customer profile refreshes. Depending on your goal { see how knowing your objective comes back in }, a different refresh or re-scoring frequency may be needed potentially at a data set level.

For example, if you are using social data to identify key life events of your individual customers, a weekly refresh might be sufficient. However, if your goal is to create a trigger event based on an abandoned cart, having this behavior refreshed in real-time is important.

# 5 Test & Learn — In the same way that a customer’s behaviors, habits, and interactions change over time, so do requirements on how you are using the customer profile data. By having a specific testing and learning plan identified prior to embarking on building your initial omni-channel views, the marketing and technology teams can better determine what elements are important for consideration.

In addition, as the customer profiles continue to be refreshed, you will be able to identify additional revenue and engagement driving opportunities. The testing and learning plan establishes the right set of performance indicators for what you are looking to accomplish.

+ + + + +

I’d love to hear from you and learn about your experience building omni-channel customer profiles. What other tips have you seen be helpful?

Drop us a note or share a comment below.

12 Aug
2013

5 Reasons You Know You’re Ready for a Data Management Platform (DMP)

And, Just Because You Have ‘Big Data’ is Not One of The Reasons to Get a DMP

Wondering where I’m going with this post because I said having Big Data is not one of the reasons to get a Data Management Platform (or DMP)? Read on …

The term Big Data is now like consultant — it means different things to different. { I’m in that group of folks that uses the term big data to describe our company … so, guilty as charged }. During a number of our meetings, the topics of data almost always comes up, whether it is about clean, dirty, big, small, or no data.

So, I thought in this post, I’d write about what a data management platform is and why many companies are moving from thinking about big data to data management.

Before I go much further, let me provide a brief description about what a data management platform is. To provide a perspective: a DMP is a system of processes and technologies that manages (or normalizes) your data to allow the business user to extract value / information easily from that aggregated data set.

Said another way: a DMP allows you to actually get your job as a marketer done (whether that is finding an insight, generating a report, building a model or executing a campaign).

How do you know your company needs a DMP? Here are the top five ways { Letterman countdown style }:

# 5 … You’ve Got More than 1 Data Source that is Updated Frequently — AT&T has Big Data because it has records about millions of us and every one of our calls logged … but so does MOOYAH Burgers (a fast-growing restaurant chain based right here in Dallas where we’re located). An important trait about the data, though, is that the information is updated frequently creating a need to continually process & normalize the data.

I’ve yet to come across a business that only has 1 data source … this particular reason means there are lots of companies out there that need DMPs.

# 4 … Your Data is Sitting With More Than 1 Vendor — Heard of the story from a marketing colleague where they say, “Well, I have to wait for agency X or company Y to give me a flat file so I can run my analysis”? Well, if you have more than 1 vendor you work with that produces data for you { think agency, social media platform, media buying, email operations, etc. } then maintaining a centralized data management platform is a strategic imperative to both ensure you have data that is well-connected but that you can also readily enable cross-channel or cross-data source analysis.

# 3 … You Have Marketing Operations that Rely on “Real-Time” Customer Data — Basically, if you have a need for ‘production analytics’, the customer data management platform ensures that your data sources are loaded, processed, and normalized. The normalized data can then be readily used for analysis, reporting, or modeling to serve as inputs for a variety of activities & functions.

Example of marketing activities that require product analytics include:

> 1:1 mobile app recommendations

> Populating executive dashboards

> Churn or attrition modeling

> Customer life cycle behavioral triggers

# 2 … You are Focused on Marketing Objectives that Drive Revenue, Engagement, and Loyalty — { This would be our # 1 except that I have an even better # 1 in our top five reasons countdown. } There are so many different data sources, lots of different tools, and more data than you can have easily tied up into a pretty little package.

The problem is, as marketers we’re spending more & more of our time getting information ready for decision making and NOT enough time evaluating the data for decision making. Consequently, our role as a marketer is changing rapidly to spend valuable resources on data vs traditional marketing tactics.

Implementing a robust customer data management platform allows marketers to focus on marketing again.

# 1 … You’re a Data Rock Star and Don’t Have a Programming / Analytics or Related Degree — You are the go to person in your team just because you were able to build that awesome analysis { in Excel } by mashing together those 3 data sets about the product, customer, and behaviors. Everyone else is in awe and you were up for multiple nights in a row cleaning and doing all of those Lookups! { You know who you are! }

Going back to # 2 above, while you are a rock star for trudging through the data and likely getting hi-fives for having a reputation of getting things done, how much of a bigger rock star would you be if you were able to spend time devising that new campaign that grew engagement 15% or innovating on an existing promotional offer set, reducing costs 10%?

It’s time for that DMP.

Have any other reasons you’d add to the list? Drop me a comment below.

08 Aug
2013

What’s Next: Personalization at the Washington Post?

Can Jeff Bezos Turn the Tide at The Washington Post by Introducing Personalization?

When I read about Jeff Bezos buying the Washington Post, I was surprised and also hopeful. I am a big Bezos fan and if anyone can transform the “newspaper industry,” I think Mr. Bezos can. Will he bring newspapers into the 21st century just as he did retail with Amazon? Will the Post begin to use personalization to engage readers with their content? Will they use personalization to deliver the most relevant ads to their readers? A large part of Amazon’s success is due to their personalization prowess, and Bezos is Amazon.

Think about it: Amazon wows its customers with their ability to know what you want and/or need, be it on the commerce or the customer service fronts. I’ve drunk the Amazon kool-aid. I probably spend 80%+ of my non-perishable grocery retail purchases with Amazon and you know why? Because I feel like they know me, like I’m a member of their family. Personalization works!

And if for some reason, they get me wrong now and again, I go online or pick up the phone and ask for help. I don’t cringe before contacting Amazon’s customer service center and our interaction never ends with me wanting to scream in frustration. (We’ve all been there with other companies’ customer service, right?) Amazon performs head-and-shoulders above its competition on both commerce and service because they have all this big data they’ve collected about me. And they use it wisely, to make my life better. My life being better > Concern about Amazon collecting my data for personalization.

But…

Can product and customer personalization be transferred to the editorial content world? That’s the story I’ll be following. If a newspaper–print or digital–could engage me with extremely relevant editorial content the way Amazon does with goods and services, they would definitely get my loyalty. And if. while reading the articles, I only (or mostly) see only those ads that are relevant for me, I would probably also become loyal to those advertisers. Probably.

11 Jul
2013

Using Big Data to Micro-Target: Integrating Big Data for 1:1 Marketing

One of the things I love about my job at Nectar is sharing with our customers how we deliver value to their business by enabling them to micro-target messages to their customers. Recently, while reading McKinsey Quarterly articles on big data, I saw a couple of statements from McKinsey directors that really resonated with me, because it addresses the core of Nectar’s value proposition.

1) From David Court: “…the key is to focus on the big decisions for which if you had better data, if you had better predictive ability, if you had a better ability to optimize, you’d make more money.”
2) From Tim McGuire: “Analytics will define the difference between the losers and winners going forward.”

I believe that if you make your customers happy, they will be loyal to you, and loyalty generally translates into greater revenue (and less cost) for your company. How do you make your customers happy? By knowing them. Know how they interact with you, know what they buy from you, know what they like about you, know who they are. At an individual level. Of course, this is easier said than done. But with the right tools and data, it can be done.

When, where, what and how you communicate with your customer is one of those big decisions. Using the data you already have from your customer and combining it with other big data–online behavior, social, CRM, mobile, etc.–enables you to predict what that customer would want to hear from you. This predictive ability, in turn, allows you to optimize your relationship with that customer, which then helps you make more money. (I use the singular “customer” because that’s what a robust Digital Management Platform enables you to do: communicate with each of your customers as an individual, yet do it at scale.)

Technology exists today to bring all of your available big data together to build a foundation from which to make your big decisions. A company like Nectar can help you bring all this data together to enable you to build 360 degree profiles of each of your customers. But we don’t stop there; we then apply our proprietary algorithms to analyze your customer profiles to determine the most relevant communications to deliver to each customer–we call this hyper-personalization. To complete the circle, we then help you distribute consistent hyper-personalized messages across every digital channel your customer uses.

We like our perch at the intersection of big data, analytics and hyper-personalization because when we bring each of these tools to bear for our customers, big decisions not only become easy but they also make you money.

Want to learn more? Please email contact@nectarom.com for more information.

23 May
2013

Using Big Data to Drive Revenue

I recently read an article called “In Media, Big Data Is Booming but Big Results Are Lacking” written for All Things D, that included several very interesting tidbits of information:

90% of the world’s data has been accumulated in the past two years.
We’re generating 2.5 quintillion bytes of data per day.
Many companies are logging and contextualizing all this information but little is happening to the information once it’s stored in the database.
And, “Even though almost every CEO says their companies are becoming data-driven, the fact is that most high-level decisions are still being made from bullet points, not data points.”

We all know that we have a LOT of data to contend with: transactions, onsite clicks, email interactions, loyalty cards, social networks, mobile apps, m-commerce, customer service calls, 3rd parties, and the list goes on. Having access to all this data is great, but it’s just a bunch of noise unless you do something with it. The key to getting value from your big data is connecting all those dots among the different customer data sets. Imagine if you could put ALL of your disparate sets of data into ONE huge database, and you have a tool that allows you to associate the data from each set with specific customers. Wow!

Think of what you can learn about your customers. Think about the value you can deliver for your customers. Think about how much more engaged your customers will be when you speak with each of them as if you know him or her. Think of how your customers will buy more from you because they are more engaged.

Now think nectarConnect. Because that’s what nectarConnect can do for your business. This SaaS product combines all your disparate sets of big data and connects the dots to give you a 360 degree view of each of your customers. At scale. nectarConnect then works with other elements of the Nectar Solution Suite

nectarConnect brings order to your data chaos. Now you can use all that valuable customer data you’ve been collecting and make it work for you. That’s how you use big data to drive revenue.