Marketing Technology

In my series on Customer Data & Decisions, I’ve explored how marketers covet that prized asset a “360 Customer View.”  In this blog, I turn things around, viewing from the consumer’s eyes what’s expected from marketing today, in the form of a marketing wish list.

The Final Countdown – A consumer’s holiday wish list for marketing

Hello Marketing, I’ve been thinking about my wish list for the holidays.   Like a kid in a candy shop, here it is:

1. First and foremost, please make sure your company builds great product and services. I don’t care how well you wrap the present, or how much money you spend on the wrapping paper.  I care about the present inside, and whether it’s useful to me.
2. Once you’re sure you have a great present inside, go ahead and tell me all about it. I don’t mind hearing your passionate stories about how great it is, if it’s really great.  But don’t take too long to do it because I’m busy and you won’t have my attention for long.  While you have my attention, feel free to entertain me.  I like that aspect of marketing.
3. Oh, and by the way, before you decide on this great present for me, make a reasonable effort to figure out why I’m possibly interested in it. Once you do that, then and only then, direct your clever marketing at me.
4. Maybe I don’t know I need your present. Fine, so tell me quickly and concisely why I need it, and what unique value it will bring to my life.
5. When you decide to tell me you have a present for me, don’t expect me to work miracles finding out about it. Make a reasonable effort to figure out where I spend my time.  Marketing, I know you are like a modern Santa, with access to all kinds of big data on my every move that gives you real-time information on whether I’m sleeping or awake; my exact location at all times, etc. – so use it wisely.
6. Take notice to when I pay attention to your marketing – what’s working for me and what isn’t, and learn from that. Like a comedian knows, timing is everything.  If I’m not clicking or I’m not laughing, then ditch the joke.
7. Once you get my attention, treat that moment like we were potential friends meeting for the first time. If you’re interesting, friendly, and we have things in common, we will probably want to meet again.   So treat our relationship accordingly.   Make an effort to get to know me before you try to close me.   Marketing gets a bad name from “ABC – Always-Be-Closing” selling.
8. Basically, don’t sell to me, help me buy. Point me to helpful information that demonstrates your present has value, will last, is fairly priced, and that others I’ll trust love it also.  If you are offering me something special, make sure there is a good reason for it being “special for me.”
9. If I’ve told you or given you obvious clues that it’s not the “Greatest Gift Ever”, respect that. In other words, don’t sell beyond the “Really I’m Not Interested Now”. If I didn’t tell you why I’m not interested – you can ask me – but whatever answer I give honor it.  Don’t push any further.  After all, just as bad as me not taking your gift, is me accepting it and later telling others how useless it was and re-gifting it because I really didn’t like or need it.
10. And finally, Santa, can you please take 15 minutes or less and talk with that Gecko Company and ask them to spend – I don’t know maybe 15% less on mass advertising next year? I just can’t get that Song “The Final Countdown” out of my head.  On second thought, never mind, I know what they will say: “If you are a Mass Marketer, you spend big bucks to get inside heads….it’s what you do.”

For interesting further related reading, and reflection on the year 2015, read “The 2015 Marketing Wish List” at http://www.dmnews.com/marketing-strategy/the-2015-marketing-wish-list/article/387692/

Comments and alternative views are always welcomed.

Note:  These views are my own, and not that of my employer

In my previous blog (“Big Data – Big Waste?”), I advocated about the importance of an upfront blueprint to help focus big data efforts in areas that lead to valuable insights.  Taking actions on these insights is ultimately how you glean value from your big data.

The 5^th “V”

Strangely, many who espouse the virtues of big data rarely start by describing the value that can come from it, but instead pontificate about its attributes – Volume, Velocity, Variety, and sometimes a 4^th V – Veracity.   Let’s talk about the 5^th “V” – Value.

What is “Value” really?

Lasting value is created when there is a positive exchange between you and your customer. Yet if you sell something that doesn’t meet their needs, you create the illusion of value, only to see it wiped out later.   Conversely, discovering and acting on activity patterns can lead to explicit and latent needs being met, resulting in customer satisfaction and lasting value.  Tracking big data effectively can lead to these discoveries.

To illustrate, if I observe a customer’s repetitious buying patterns, and then offer to sell products in bundles, bulk ship them for less, or proactively send them, value is created for them – and you ensure a future purchase stream.

Let’s look at some examples of what firms are doing to unearth insights, take actions, and create value.

Real life examples

A leading automotive firm installs numerous sensors in high end vehicles that gather driver data –   predictive analytics warn drivers when fatigue might be setting in.   Farmers use wearables on cows, improving fertility and birthing success rates.

A major online retailer uses purchase history to predict what products you are likely to buy in the future, and stages those closer to reduce shipping time.  A travel site can monitor real-time flight activity, anticipate delays, and notify travelers – often before the airline does.

These are but a few examples of the way firms are already using sensors, streaming big data, finding actionable insights, and creating value for their customers and them.

What should I do?

Ask yourself two simple questions:

1. Is my company using modern data collection, streaming big data in real-time and using predictive intelligence to understand the patterns?
2. Are we taking immediate action on these insights to enhance the customer experience?

If you answered yes to the first, but not to the second, you have the infrastructure, but without action will get no benefits.  If neither is true, you are falling behind by the day.  But it’s not too late.  Act now for value.

You’ll need a unified system that can ingest structured and streaming unstructured data, perform real-time analytics that monitor for patterns, decision strategies that arbitrate and trigger the right actions when unusual opportunity or risk is detected – and a system that can also automatically kick off processes to alert personnel, open a case, or notify customers.   Make sure to find a system that can give you this in one platform or otherwise you will waste valuable time implementing, integrating, and adjusting various pieces when you could have been creating value.

Comments and alternative views are always welcomed.

Note:  These views are my own, and not that of my employer

Part 1 was about what firms really do know about me as a customer. Part 2 covers the ever popular topic of Big Data and why it needs a sponsor, action plan, and a solid analytic platform.

What did you say?

Did I get your attention with my somewhat controversial headline? Maybe it’s actually not that contentious because simply wiring to and capturing lots of data (e.g., Big Data) does nothing for you except add cost if you don’t effectively glean insight from it, and take action on it.   It’s no different than any other asset, in that if it’s idle, it’s sucking energy and not providing any return.   Like a big data black hole, where data enters but no insight can escape. How do you combat that?

Have an action plan based on the kinds of customer decisions you want to improve, investigate data required, and constantly test, monitor, and refine that plan.   This plan will dictate what data you should be seeking and exactly how you will leverage it. In other words, work backward from your desired outcomes.

You might also be asking, what is big data? Good question.   As a participant in the business intelligence revolution, I’ve seen massive databases used for years for decision purposes. So what is new and different?   Actually, there are a few things.

First, customer data has been historically captured, scrubbed, matched and restored into on premise structured databases.   This led to the enterprise data warehouse with the so called “360 degree view” of the customer.   These systems required data expert intervention to add new data elements, were usually on premise, and latency rendered the view stale for today’s standards. Consumer and market expectations have evolved to expect on-demand and streaming data reflecting the latest and greatest view of the customer.

Second, since it ultimately required a target structured store, unstructured data, which is massive, became difficult to assimilate into one structured data warehouse.

And third, the variety of structured and unstructured data sources have grown, so much that again using an approach of trying to codify and blend all of that data into one mart did not meet flexibility, agility, and timing requirements of business people trying to make better decisions.

Ok, I need a plan. What next?

What if you could identify and sway vocal and influential customers? What if you could proactively identify customers at risk, and take actions to not only save the relationship, but turn them into ardent supporters?

Take these types of questions, and work backwards to formulate your plan.   Call it your big data blueprint.

Do you already know who the most influential customers are?   If not, start there.   How would you define this?   Conventional wisdom may first suggest it’s those with the biggest network of followers or highest NPS score. But upon further review, what might be more important is customers that actually frequently refer versus ones who say they will.   Working back, you would need data like mentions and referral codes. So determine the particular outcome, and then concentrate on connecting to the data you need to monitor and track those actions – viral actions such as re-tweets, re-posts, forwarded links, reference events, and such.   Then, rate your customers on that basis – building a Clout Score – the higher the score, the more clout they have with others and the more they refer you.   This score is then connected with actual behavior instead of formulations, surveys, or postulates.

Likewise, figuring out which customers are at risk, you might hypothesize that a major service interruption would put them at severe risk, and thus simply being able to run a query to find all customers impacted by key service disruption events would suffice.   Yet often, customer retention risk is much more complicated than that, and it’s likely that in this case you need a behavioral model that considers various risk factors, such as service disruption patterns, social sentiment, clout, customer loyalty, competitive options, and switching costs – and then test that model against real churn outcomes to calibrate its effectiveness.

Having sponsors is vital because invariably some aspects of your big data plan will involve capturing and leveraging data not readily available, and thus sustaining funding and resources to see your project through will require champions – people who believe strongly in the cause, and can help.

What technologies can help me get to my happy place?

We don’t live in a simple world.   We accept that, or get lost, frustrated, and fall behind – but we do expect technology will continue to help us navigate the intricate world.   So we seek the simplest and fastest solutions to complex problems.

Your answer is you will need many technologies. Accept that and do business on that basis. Select your stack based on requirements that your vendors are open, constantly invest in innovating their underlying technology, have exceptional integration both with their own sister products and with the outside world. Consider firms with a robust ecosystem and strong reputation for training, partners, and professional services.

Big data systems involve storage and retrieval of unstructured information, which is data that has not been highly codified from its raw form.   For example, data entered into free form text such as comments to blog posts or data collected from digital activity such as granular website click activity.   Big data is also real-time streaming data coming from various sensors that are always on, and stream data (often 24×7) such as devices that report precise location of objects (e.g., mobile phones).

Partner with a vendor that has solid, modern, and open technology, and has it in one platform. Beware of companies that get the marketing right and have compelling messages, slides, and even case studies, but under the covers have 2 or more actual platforms stitched together, and requires more custom coding to meet your requirements. How ironic that I’m warning marketers to be wary of the marketing!

Comments and alternative views are always welcomed.

Note:  These views are my own, and not that of my employer

In this post, I explore what firms really do know about me as a customer.

What do they know, and how did they get that?

Wow! Where do I start?   Many companies today are doing everything in their power to amass as much data on me as possible so presumably they “know me” and make more relevant marketing offers…or as they say, “to provide me with an exceptional experience through an ongoing conversation.”

As empowered consumers, we are the judge of whether they are doing this well. Are they really capturing the right information and in a way that is respectful, well-timed, and used appropriately? This subject quickly stretches into ethical and political implications, but to avoid that, I will just lay out some facts about what companies are doing:

• First and foremost, they save everything I do with them. All interactions, all transactions, all orders, all clicks on their website – basically any activity on their physical and digital properties they capture and keep – in some cases for 4 years or more.
• They can often freely share this information with subsidiaries and affiliates (which means a lot of other companies) unless I explicitly ask them not to.
• They append 3^rd party data – lots of it. The sharing of data about me is ubiquitous. Appending means other companies are capturing data and sell it and it’s often indicative of my affinity to like, want or to buy something. This can pretty easily be matched to me with a presumption, for example, I also like to golf because I subscribe to a golf magazine.
• They are looking at my patterns of activity.
• They progressively profile through very short but repeated data collecting. For instance, I sign up on a website and provide basic information, then I agree to a news letter, and they capture some preferences, I download their mobile app, and so forth. Eventually, they may know whether I own or rent, have children, or are planning a kitchen remodeling.
• And they try to predict their next best move. In other words, they are trying to figure out what I really need and want. Called “Next Best Action” technology, and usually found in larger companies, there are very large teams tasked with calculating lifetime value, building rules, testing propensity models – and ultimately a hub that makes promotional, product and service recommendations.

Really very little of this is new my friends, it’s just massively accelerating.   In 1992, one of my favorite non-fiction authors, Erik Larson, wrote a book called “The Naked Consumer: How Our Private Lives Become Public Commodities.”[i] Back then, his impetus for writing the book was based on a pretty simple event driven mailing he received for his child’s first birthday. Intrigued, he chronicled a world he saw as already borderline out of control with consumer data sharing. Imagine his sentiments now with the growth of the internet, digital channels, social, mobile, and big data.   I think he might change the title to “The MRI of the Consumer.”  This month, Scott Brinker posted a blog entry estimating that nearly $22 billion USD of venture capital funding has been poured into the marketing technology companies he pastes onto his marketing technology landscape and admits it’s probably underestimated.

As a long time marketer, I’m not that paranoid or really that appalled at what is going on. I still believe we live in a world that has checks, balances, methods and free choices.   Often, as consumers, we decide how much information to give up in return for something.  In most cases it’s a conscious choice. And there are ways to combat and prevent abuse. My biggest concern is security, as information is repeatedly hacked and then used for purposes it was never intended for. Better security and education are needed, but in general I don’t think it’s as surprising today to the average person as it was for Larson 20 years ago.

But rest assured, this picture of you is getting clearer – and there is a substantial amount of corporate energy being poured into filling in the blanks. The popular term is “The customer journey”, and now also being called “The customer movie”, with the intent to define every frame.

Yet motives and reality are two very different things.   I might want to be rich, but simply wanting doesn’t make it so.   And really, who I am versus what specific habits or preferences I have in relation to a certain product or service is generally where the line is drawn. For instance, a home improvement business would love to know what kinds of building skills I have, what tools I have, and what projects I’m considering, yet I don’t think they really care about what music I like.

What do they intend to do with my information?

I believe at the heart, companies just want to sell more of what they have and do it at the lowest possible cost to them.   It’s that simple.   But they know the world is competitive, there are choices, switching barriers have eroded, and if what they are offering (or failing to) isn’t a match, or at the right price – I will go somewhere else.

So they collect data, study events and patterns of activity, test timing, try to get preferences right, personalize content, and hope I’m impressed when they take actions.

Are they becoming specialists majoring in knowing one aspect of me, and knowing it well?   Perhaps, but make no mistake their not your best interests are in mind, and if information is useful to another party, and a business transaction makes sense, it will happen. Ironically, businesses are better at sharing customer information then the healthcare industry is at sharing patient information, although finally we are seeing some improvement there.

How did they do that?

It’s really not rocket science, yet amusingly marketers are applying technology that is also used to help launch and guide rockets.

When a rocket launches, there are sensors monitoring all its complex systems. As a consumer, your systems – what you say, what you do, where you go – are being monitored.   Hotels are now placing beacons at key locations such as the front door, to detect when you arrive.   Stores are using similar technology to gauge your potential interest in a product sitting on the shelf you are next to.

There is already software and technology, and the cost is dropping, to gather this data and allow the marketers to access it and build rules on it (e.g., if customer arrives, alert front-desk personnel and pop-up appropriate offers).

Rocket scientists make heavy use of statistics and probability theory to understand the amount of redundancy necessary in systems, the likelihood of something failing, or predictions of weather to gauge best launch and landing windows. Marketers use all these techniques to tune their systems for response time versus cost, whether a new promotion will succeed, or timing a communication.

Also, the cost of storing, aggregating, distilling, modeling, and using this information is dropping rapidly.   The internal discussion has shifted from how much data should be saved, to how more data can be synthesized and insights gleaned from it.

A confluence of freely shared institutionalized best practices, application speed and simplicity, cloud computing, automation, and scientific testing procedures has led to more companies with access to better marketing technology – and a better, albeit still incomplete picture of you the customer.

Comments and alternative views are always welcomed.

Note:  These views are my own, and not that of my employer

[i] Larson, Erik. The Naked Consumer: How Our Private Lives Become Public Commodities. New York: Penguin Books, 1992.