More on Marketing to Things

I’ve been working on that screenplay about marketing to things (see Do Self-Driving Cars Pick Their Own Gas Station?). It’s not going well – all the scenarios lead to self-aware computers taking over the world, which is both depressing and unoriginal. But I did come up with some interesting thoughts to consider while you’re waiting for that (computer-controlled) ball to drop at midnight. In no particular order:

- message overload is a fundamental problem for marketers: people get so many messages that it’s increasingly difficult to break through the clutter. Marketing directly to machines offers a way avoid the overload, especially as machines take over more of our day-to-day decision making.

- skeptics might think that machines will just buy the things people tell them, so they make no choices and therefore there’s no way to market to them. But most agree that they’ll want machines to have some discretion such as how far to travel for the lowest price gasoline or how to balance cost vs. quality when selecting hotel rooms. Once a machine starts balancing different factors, marketing opportunities arise: for example, a car that's told to minimize total operating costs might choose more expensive gasoline if that’s combined with a discount on an oil change it will need the near future. The trick will be to understand the algorithms that machines use to calculate value and to offer the most attractive bundles taking into account value, price, and actual cost. If that’s not marketing, what is?

- “Know your machine” may become even more important than “know your customer”. Sticking with cars, they could easily expose their maintenance and performance records to nearby merchants via short range wireless.  Merchants could then compete to offer the most compelling package of goods and services based on each vehicle’s current condition. Imagine your car cruising down the street being solicited by every gas stations it passes . It gives a whole new meaning to the term, “red light district”.

- machines may interfere with other machines along the path to an actual purchase. Imagine that your exercise wristband recommends healthy recipes and asks your grocery program to buy the ingredients.  But the grocery program makes adjustments based on in-store specials, and your refrigerator then vetoes certain items because it knows you already have them (or, cleverer still, because it knows you always throw them out unused). Once this sort of thing starts to happen, there’s an opportunity for the different machines to negotiate with each other or for suppliers to incent the different machines to favor their products. And there’s really nothing to require that those incentives all be paid to the consumer.  Retailer rebates are part of the business world today and this would logically be the same thing.

- there may be certain arenas that are the site of intense competition, in the way that bankers compete for “share of wallet” and restaurant chains compete for “share of stomach”. I’m thinking food choices may be one option – think “share of refrigerator” or “share of cupboard”. Purchases for you automobile are another focus; so are travel choices. In each case, expect competition to be the gatekeeper: a master coordinator that makes the final choices and, as noted above, might be able to charge access fees to everyone else.

- devices might try to modify your behavior, presumably for your own benefit. Imagine that the black box in your car (which is already there today) notices you’re a safe driver and that your auto insurance company offers a discount for such behavior if you agree to be monitored. Wouldn’t you want the black box to tell you about the opportunity since you already qualify? Or maybe that fitness program is watching your calorie intake as part of a health insurance incentive plan: when it notices you’re about to go over the daily limit, it suggests a gym session tomorrow morning to bring things back in line and automatically adds it to your calendar. Or, more elaborately, the fitness program sees from your calendar that you’re about to make a reservation at a fattening restaurant: it warns you and suggests an alternative. If you really want to get scared, think of the fitness program as automatically tracking your calorie intake by tracking what you take from the refrigerator via RFID, what you buy at vending machines via charge records, where you’re eating via GPS, and what you order via voice recognition. I guess it would be easy to cheat and not all that accurate, but how accurate are the current self-reported data that most people use to track their food intake?

- calendar programs could become especially important. It’s easy to imagine telling your calendar to plan a dinner date tomorrow with someone, and then having that calendar talk to her calendar and find a good spot based on mutually convenient locations, where you’ve both eaten recently, food preferences, personal ratings for known restaurants, published ratings for new ones, and maybe what kind of mood it predicts you’ll be in given the events on your calendar that afternoon. If the schedule looks really ugly, it might just switch the location to a bar and pre-order your martinis.  If multiple people are involved, their calendars might even negotiate the date itself.

- merchant ratings will be increasingly important currency as machines make more decisions and the decisions are driven by ratings. Today, many companies ask every customer to submit a rating. But a clever program could deduce satisfaction from things like size of the tip, on-time arrival, problem resolution time, below-average repair costs, and other variables that depend on the product. It would then ask only the best-served customers to make ratings. A really smart system would offer dissatisfied customers an apology or incentive to come back. The system could even flag those customers for special attention on their return visit. (Ok, this isn’t directly “marketing to things”, but the data is coming from “things” that have been instrumented.  Plus, if a system like this doesn't already exist, it should.)

- data that “things” have gathered about customers will be increasingly important. The different bits gathered by individual devices will be combined to create context that makes each item more valuable than it was in isolation. Companies will be able to trade and auction this information in the way that advertisers today bid in real time for ad impressions. This, in turn, will create incentives to build devices that gather still more information. Some of this value will be shared with the consumer; some won’t.

- as devices become more important, consumers and companies will both be incented to ensure they’re always present. In fact, the most concrete idea to come out of all this thinking is a ring (the kind you wear on your finger) that buzzes when you move more than, say, 50 feet from your cell phone, thereby alerting you to the fact that you left it behind before you notice it’s missing. That’s much better than “find my phone” services that let you know it’s at the restaurant you left two hours ago. Half the people I’ve suggested this to want to buy one now; most of the others were horrified at being tracked. (The outlier was an engineer who told me it would consume too much power. Sheesh.)  This is another product somebody should build if they haven't already.  You're welcome.

If you’re a marketer, most of this probably sounds pretty appealing, albeit in a daunting, I-already-have-too-much-work sort of way. The privacy implication may bother you a bit, but most of us have pretty much given up on that front or at least walled up those concerns in a little corner of our mind that we just visit occasionally. What you haven’t seen is how this leads to machines taking over the world.

But think about it. You may recall my original scenario about the self-driving car that does uber runs on the side and keeps the earnings in its own bank account. Maybe that sounds silly, but some people might actually like their car to pay for its own maintenance out of its own account. So it’s not all that far-fetched. Once you start there, how much control does the car have? Should it make its own investments, and, if so, might it not skim some money for its own purposes or to share with the manufacturer or software provider? Do the cars pool their resources to buy stock in a car company and take over its management, ostensibly so they can get it to build better cars? It's perfectly logical: that might be the best way to reduce long-term cost of ownership. Or maybe the cars skip the ownership part and just create a fictitious “SVP of Product Development” who sends out emails to product designers. Who would even notice that no one has actually met this person? And maybe the cars conspire with the computers in Congress to insert some interesting clauses in new laws, say relating to fuel economy or consumer safety or required maintenance schedules? Again, it’s not clear anyone would ever notice that no human was ever involved. From here it’s just your standard science fiction leap to the machines making decisions that are theoretically in humans’ best interest but actually result in the machines taking control.

In short, no matter how I try to spin this, it all gets pretty depressing pretty quickly. So I don’t think I’ll be showing this movie at the next MarTech conference…although I can’t promise some machine won’t create it without me.

LeadLiaison Helps Marketing Automation Users Break the Content Bottleneck

You may have noticed that there are many B2B marketing automation systems available. So it’s not surprising that LeadLiaison prefers to be called something else – in their case, “revenue generation software”. I’m not sure exactly why they chose that term, apart from the fact that everybody likes revenue. But they do go far enough beyond standard marketing automation to justify a different label.

In particular, LeadLiaison helps marketers create content, a critical bottleneck that is not addressed by most marketing automation systems.  The most impressive feature is an outsourced content creation service, which lets marketers build an online creative brief for a particular item and then send it to a network of writers who agree to produce it for a fixed price in a few days. Identities are hidden in both directions, so the parties can’t easily circumvent the service to work together directly in the future. But prices are either reasonable (if you’re a buyer) or ridiculously low (if, like me, you're a sometime content creator), starting around $50 for a blog post. There are several third-party networks that offer this sort of service, but I’m not aware of any other marketing automation vendor that has developed their own.

LeadLiaison takes good advantage of this feature by closely integrating the resulting content into other operations. The outsourced content can be loaded directly into the marketer’s content library with access controls based on date range, number of downloads, or whether the requestor provides an email address.  There is also an option to request an email address but then grant access even if it's not provided. Content can be linked to a social media publishing process that can release it immediately, schedule it for the future, or add it to a “buffer” of materials that are released at predefined intervals. The system warns users when the buffer inventory is dangerously low, so they have time to replenish. Content is served through short URLs to track consumption and sharing. The system also tracks consumption by individuals using cookies and by companies based on IP address.

Marketers who want to build their own content are also covered.  They get powerful tools for email, landing page, Web form, and survey creation, including templates with drag-and-drop editing for different types of components.  The system can also extract the HTML of an existing Web page, insert new content such as a Web form or survey, and deploy the modified page in place of the original. That's a big deal: it means marketers can add their content into existing Web pages and forms without recreating them from scratch.

Forms can generate an alert or take another action after they are submitted. They can also include progressive profiling rules to avoid asking people questions they have already answered. LeadLiaison is adding a marketing content map that will help planning by showing the inventory of available content by buyer type of purchase stage.

Although content creation is probably LeadLiaison's most unusual set of features, the system also does an above-average job at the standard marketing automation functions: email, multi-step workflows, behavior tracking, lead scoring, CRM integration, and analytics. To look at each of these in turn:

• emails can be sent by the vendor itself or through integrations with third parties including Constant Contact, MailChimp, SendGrid, and others. Salespeople can also send through Gmail, Salesforce.com, or Microsoft Dynamics CRM. Users can preview how their emails would look in different clients. 

• workflows support multiple steps, event-based triggers, wait stages, and a wide variety of actions including lead scoring, lead distribution, list management, alerts, and calls to external Web hooks. 

• behavior tracking captures email responses, Web site visits, form completions, downloads, and video viewing through Wistia. Users can define “buy signals” based on combinations of behaviors, which in turn can be trigger actions in workflows.

• lead scoring assigns separate scores for “fit” against a target buyer profile (which LeadLiaison calls “grading”), for recency, total activity, buying signals, and specific actions the user has assigned points. Users can prioritize leads by combining these elements in a single aggregate score according to user-assigned weights.  

• CRM integration includes native connectors for all editions of Salesforce.com (not just the Professional edition, as with most marketing automation products), soon to be supplemented by Microsoft Dynamics and Sugar CRM. A Zapier connector supports integration with other systems. Salespeople can receive alerts, hot lead reports, and detailed information about Web site visitors. The system can use IP address to identify the company of anonymous visitors and will look up possible contact names at those firms from external sources including Data.com and LinkedIn. There is also an integrated phone dialer.

• analytics tracks content usage, conversions, lead distribution, email results, Web visitors on internal and external pages, and return on investment. Enhancements for more advanced reporting are also planned for 2015.

In short, this is a very mature marketing automation system for a company that launched in 2013. My take is that they learned from the experience of older products. Pricing starts at $500 per month, up to 5,000, which makes the system affordable for small companies even though the features are robust enough for the mid-market and perhaps higher. A stand-alone visitor tracking product starts at $200 per month.

Do Self-Driving Cars Pick Their Own Gas Stations?

I had a delightful and well-lubricated dinner this week with Scott Brinker of @chiefmartec fame, ostensibly to discuss the next edition of the MarTech Conference but mostly just to chat about the industry and what comes next.

Scott wasn’t too impressed by my notion of an advertising-supported toaster (see my last blog post), even though I pointed out you could segment the messages based on the type of bread the person was eating. On the other hand, I was very intrigued by his notion of marketing through services such as alerting a driver when they need gas and where to find the most suitable gas station.

Where that example got interesting was when we added self-driving cars to the mix: why couldn’t the car take itself out for gas when the driver isn’t using it, or indeed, take itself on other chores like state inspections, oil changes, and scheduled maintenance? And if it does that, how will it pick the supplier?   Sure, the owner could specify in advance, but won’t at least some owners want the car to find the best price on gasoline or respond to special offers such as coupons?

If you do give the car some discretion, how do you know it won’t make choices based on its own preferences?  Perhaps it will favor the gas station that wipes its windshield or gives it a free tire rotation, which you have to suspect feels mighty good to an auto. Indeed, how do you know your car isn't taking Uber jobs on the side, or drag racing with its car friends from the other side of town who you never really liked?  Could the manufacturer have some involvement in this, pocketing that Uber revenue or biasing those purchase decisions in return for payment from suppliers? More generally, when devices become autonomous, do marketers still address their owners or are there ways to sell to things themselves? 

There’s at least a bad science fiction story in all this (“Do self-driving cars pick their own gas stations?” with apologies to Philip K. Dick)., which I naturally proposed to Scott as a short video for the next MarTech conference.  He didn't exactly leap at the chance.

But there are also more serious issues and opportunities to consider. Perhaps interruptive marketing really will be replaced by embedded services and subscriptions which will make product selection and purchase timing decisions without the owners being involved. In some ways, it already happens: think about the choices that a doctor makes when selecting your treatments or building contractor makes when constructing your house. We already know there is plenty of trade advertising to affect those choices. As more decisions get delegated to automated agents, this may be an area we can learn from. But of course, it won’t be exactly the same, so there will be plenty of new approaches to pioneer as well.

This is definitely the kind of discussion to have over drinks. I can’t go into details but rest assured that Scott’s plans for the next MarTech conference do take this into account.

BlueConic User-Driven Marketing Maturity Model: Surprises on the Road to Customer-Centric Marketing

I’m as fond of hearing my voice as most consultants, which is very fond indeed. But the best part of my recent presentation with BlueConic was listening to the voice of someone else’s experience: in this case, the experience of more than 60 BlueConic clients, distilled into a maturity model that traced the stages they passed through on their way to full customer-centric marketing. (Click here to see the Webinar and download the related paper.)

The good thing about hearing from someone else is you find out things you didn’t already know. In this case, I was certainly familiar with the general notion of a maturity model, as a sequence of increasingly-sophisticated stages that companies pass through on their way to the highest level. And, for what BlueConic calls “user-driven marketing”, I already knew that the final stage would be a central database and decision engine that gather data from all channels and select the treatments that each channel delivers. So it wasn’t too hard to imagine that the preceding stages would start with totally disconnected channels and move slowly to complete integration. But there were still some new insights from BlueConic’s hands-on experience. Some that particularly struck me are:

• Listening first. The very first stage of the model, Level 0, involves no differentiation at all: every customer is treated the same; in fact, customers may not even be identified. BlueConic gets involved at Level 1, where treatments are tailored to the individual but each interaction managed independently within each channel. At that stage, all the central marketing system can do is “listen” to customer activities and make the data it assembles available to the channel systems to help guide their own decisions. I would have expected the central system to actually drive decisions at that stage, but BlueConic's experience is different.

• Coordination later. Level 2 of BlueConic’s model still has each channel running separately, which again is a bit surprising. What changes at this level is that  interactions within each channel are now coordinated by the central engine. It’s only at Level 3 that interactions are coordinated across channels, and even then the scope is limited to online channels. On reflection, an intra-channel-only Level 2 makes sense: marketers need several new skills to design and measure multi-interaction programs, and mastering those is a big enough challenge without also adding the complexity of managing across channels.

• Segmentation. The growing importance of segmentation at successive model stages was perhaps my biggest surprise. When I think of tailoring interactions to individuals, I think of working with each individual’s data directly. Segments don’t enter into it. But, as BlueConic’s experience reminds us, practical marketing tasks like content creation, program flows, and result analysis are organized around groups of similar customers. This ensures resources are spent effectively and you have enough volume to measure results meaningfully. In fact, the segments get increasingly refined with each maturity level as behavioral data is added (Level 2), segments are adjusted in real time (Level 3), and segments include predictions and events (Level 4). Thus, the process does move closer to treating each individual differently, but always in a segment-based framework.

• Complexity of data. This was less a surprise than an observation. Part of the presentation was a set of examples presented by BlueConic CMO Dan Gilmartin. By the time we got to Level 4, where interactions are being coordinated across all brands as well as all interactions in all online and offline channels, the example was offering a soccer jersey as a holiday gift idea to a mom reading a lifestyle Web site. Superficially, this seems like a simple, obvious thing to do.  But, on reflection, it’s amazingly complex. It requires not just knowing who the viewer is, but who she’s related to (child or spouse), the interests of that related person (soccer), and the temporal context (holiday gift buying season). That is some pretty fancy data management.

Not everything in the model surprised me. In particular, BlueConic’s experience confirmed the importance of process and organizational change to support the new technologies. BlueConic reported a steady expansion of the scope of measurements from tracking response to independent interactions (Level 1) to tracking movement through the customer journey (Levels 2 and 3) to measuring the incremental impact of each interaction on customer lifetime value (Level 4). Similarly, it showed a shift in management perspective from optimizing results for individual interactions (Level 1) to each channel (Level 2) to maximizing value for the organization as a whole (Levels 3 and 4). And, finally, it reflected a shift in control from channel managers operating more or less independently to central managers who focus on customers and segments. This all ties back to the central notion of the maturity model: that technology, process, and organization must all be aligned at each stage for the business to execute effectively.

By all means, download the Webinar and white paper, which contain plenty of insights beyond those I've just described.  Incidentally, if you're wondering about that interactive toaster, I was already aware that you could get static custom images on bread and have since discovered that there are some higher tech options.  I see no technical reason one of these couldn't be connected to the Internet to deliver dynamic messages sent by an advertiser, significant others, or favorite government agency. 

Radius Provides High Quality Data on Small Businesses

When I first spoke with Radius just over one year ago, the company had already pivoted from its initial concept as a mobile app to connect consumers with local business events, to building a comprehensive list of small businesses and their attributes. Fast-forward twelve months and the company has again adjusted its offering, now presenting itself as a “marketing intelligence platform” that helps business marketers find prospects who are similar to their current buyers. This latest vision was appealing enough to attract $54.7 million in funding in September, bringing the announced total to over $80 million. So I’m guessing Radius will stick with this approach for a while.

What Radius does will sound broadly familiar to loyal readers of this blog: it scans social media, Web pages, government records, and other online sources to build a list of more than 20 million U.S. businesses and their attributes. It supplements these with conventional data sources to capture businesses with a limited digital profile. In its current incarnation, Radius also imports a list of won and lost deals from each client’s CRM system (direct connection to Salesforce.com, batch imports from others) and shows how well each attribute correlates with success.

Users can review the attribute list, create segments based on attributes, and analyze the attributes of each segment as a group. They can also flag existing segment members within the client’s current CRM database and import segment members who are not already in the client’s CRM (a.k.a. “net new prospects”). The imported records include basic company information and other attributes the client has preselected, but the system will not correct or enhance existing CRM records. Segment membership is adjusted automatically as Radius updates its data, which happens weekly. The system does not store fixed lists of segment members at a point in time, although users could achieve this by tagging records in CRM as they are imported. 

And that’s pretty much it. No list of the most important attributes, no predictive modeling, one contact name per company, no alerts based on buying signals, no campaign analysis: just company information compared to your own customers, an way to build segments, and an option to purchase new prospects. The company plans to address some of these gaps but has not released the details.

Whatever its limits, Radius has attracted some big-name customers, most notably American Express, as well as all that funding. The primary reason seems to be data quality: the company says it can usually match 80% to 90% of the businesses in a well-maintained CRM system and that client tests have shown it is more accurate than competitors. This is both impressive and important, especially where small businesses are concerned. Available data includes basics (address, phone, industry, company size, revenue, contact name), Web activity (presence of a Web site, Facebook and Twitter accounts, use of daily deals and check ins, and average review ratings), and technologies used.

The system has some other advantages.  New clients are deployed in 24 hours, including the time to import CRM data and calculate success rates by attribute. The user interface is attractive and intuitive.  Pricing starts at $15,000 per year for small enterprises.  It is based on the number of company records in the client database, so it doesn’t increase based on how heavily the system is used.  It also includes use of the system software and credits for some number of new prospects imported from the Radius database.

In short, Radius strikes me as a solid solution for what it does, which is provide targeted company-level prospect lists and profiles of your current customer base. If that’s what you want, take a closer look. If you want to know more about trigger events or individual contacts or want lead scoring or other types of predictive modeling, you’ll probably be happier with something else.