Financial Institutions Can Put Artificial Intelligence to Much Better Use

I’ll start with a potentially controversial statement. Banks are misallocating their investment in artificial intelligence and predictive analytics by putting it into consumer-facing chatbots, rather than using it internally to empower their staff to understand and better serve the customer.

I’ll start with a potentially controversial statement. Banks are misallocating their investment in artificial intelligence and predictive analytics by putting it into consumer-facing chatbots, rather than using it internally to empower their staff to understand and better serve the customer.

Most customers don’t like speaking with bots and usually call their bank when they have an issue that requires processing that’s beyond what artificial intelligence can currently offer. In fact, AI’s reputation has been damaged virtually beyond recovery by the endless loop most customers encounter when they call the bank, not able to get to where they want to go.

Moreover, you don’t see pictures of chatbots pinned up in banks with “Employee of the Month” emblazoned across the bottom. Nor was any new business won on the strength of a chatbot’s performance. Finally, customers don’t stay with banks because they developed a great working relationship with a chatbot. Truth of the matter, chat hasn’t reached the level where it’s consistently reliable for addressing the customer concerns that rise to the level of making a call to a financial institution.

All that said, artificial intelligence is a highly powerful tool. How it’s being used is simply being misallocated. So the question becomes, is there a way banks can use it to enhance human engagement with clients? The answer is, “Yes.” Although banks and other financial institutions are in a completely different line of business than, say, a luxury retailer or car dealership, what they have in common is that critical need to engage customers at various points in a given transaction. This applies to banks and other financial institutions at least as much as it applies to other businesses. Reaching out to, connecting with and maintaining relationships with customers, and doing it well, is a key consideration. Done well, banks have a better chance of securing a higher lifetime value from their clients when they get it right. And it’s much harder for bankers or advisers to know about the hundreds of products that are available to them; far more so than, say, a car salesman at a dealership, or an associate in the dress department at Saks. AI’s best use is providing them — the customer-facing bank advisers — with the tools to have the right information for the right client, so they can spend more time on the customer relationship.

There are ways in which the power of predictive analytics can be brought to bear immediately, creating a more substantial and recognizable benefit for both financial services providers and their customers. A knowledge-driven approach to cross-selling and upselling is one such strategy.

There’s a vast range of training, tools and processes that can positively influence engagement efforts. But predictive analytics can push these initiatives into a much higher gear, providing a uniquely powerful impact when it comes to solidifying those all-important bonds with customers. Through better analysis and use of data that’s already available to most financial institutions in petabytes, it’s possible to learn more about customers, and consequently offer them more relevant service, support and product options. The right, internal approach to applying predictive analytics, therefore, results in benefits for both customers and the financial services providers they work with — a true win-win situation.

Historically, banks — especially large ones — tend to lean more toward conservative, careful approaches to new strategies and technology than quick movement and adoption. Given the mound of compliance mandates that govern their every engagement, this is understandable. But it but can be a significant drawback. This is where predictive analytics can sharpen their game. Many institutions have demonstrated a resistance to adopting this specific tool, or have used it in a very limited way. But they’re missing out on the benefits. And understanding the inherent pitfalls in predictive analytics is key to achieving success in deploying it.

How Financial Institutions Can Effectively Deploy Predictive Analytics

It’s a given that cross-selling and upselling help create more lifetime value from customers. But finding strong connections between products and clients is still a complicated process; particularly when you have to juggle moving parts, such as customer credit scores, income, credit utilization, and the like. Figuring out what products you can sell to whom, and predicting what those outcomes will be, constitutes a successful cross-sell. When done correctly and ethically, cross-selling can ultimately strengthen the customer relationship into a lifetime value — read, profitability — for the bank. This is because they’re able to match a product that was needed with a demand that they’ve identified.

It’s 20/20 hindsight, but we all know about the debacle of Wells Fargo’s unethical cross-selling and upselling, and how much trouble it got into as a result. With upselling, predictive analytics can really make a difference in the campaign to upsell. And unlike the Wells Fargo situation, this approach is sustainable. Looking through vast amounts of consumer data can help banks to understand how relationships have historically evolved between the bank and its consumer over time. On the consumer side, the spotlight is on how their data is being used. Only by robust analysis of customer behavior — ideally where multiple products are being offered — can banks regain their customers’ trust that their data is being used to benefit them.

Predictive analytics platforms can conduct this type of analysis, leaning on demographic information, as well as purchasing and financial data that institutions already have from past customer activity. All in real-time. Such an analysis would be prohibitive in terms of time, were trained experts to do the crunching. The predictive analytics tool can then offer sharply defined, personalized, relevant recommendations for staff members to share, while they continue to provide the critical human element in the cross-selling and upselling processes.

Where does this data come from? The sheer volume of payments data that banks gather, whether credit card, utilities, rent or many more — can inform what financial product the customer might be looking for and can afford, creating a sharper, more relevant offering. And that’s where artificial intelligence and predictive analytics can play a role that helps bankers sharpen their game and engage more successfully with their customers, without throwing them on the mercy of the bots. Incidentally, it also proves the notion that artificial intelligence is less about displacing humans and more about helping them perform higher-value work.

Securing profitable customers — back to the lifetime value concept — is job No. 1 for banks, whether small or large. Successfully cross-selling — truly matching a product with an identified need — goes a long way to strengthen that customer relationship. The current financial services landscape is ripe for improvement through the use of predictive analytics. Many institutions are already using advanced analytics, tied to marketing and basic interactions — but few have developed strong processes that focus on understanding customer habits and preferences. From there, they can use predictive tools to become more relevant, valuable — and humanly available — to their clients. The institutions that manage to do so will have an advantage in building stronger, longer-lasting relationships and will enjoy the increased value that comes from them.

With thanks to Carol Sabransky, SVP of Business Development, AArete, who made substantial and insightful contributions to this article.

Can Marketers ID a Budding Customer Relationship?

Many marketing departments are shifting from sales conversion to a more balanced relationship focus as their primary objective. As a result, there is increased focus on customer experience and customer loyalty.

Many marketing departments are shifting from sales conversion to a more balanced relationship focus as their primary objective. As a result, there is increased focus on customer experience and customer loyalty.

When it comes to measuring those efforts and related KPIs, however, most marketers are still thinking from a sales conversion perspective. Obviously, this is a problem, because KPIs influence most business decisions.

2 Common Oversights Preventing Proper Customer Relationship Identification

  1. Taking Credit for a Sale and Not a Relationship. Most marketers don’t take credit for the full lifetime value of their new customers. Rather, they are primarily focused on the sales conversion for each campaign. While lifetime value can be multiples larger than the initial sale for subscription type business, it can still provide a 30 to 60 percent increase in ROI for most other businesses. Alternative long-term measures, such as retention or repeat visits, are also helpful — but lack the holistic perspective of LTV. This is because they bifurcate the relationship between new business and repeat business and leave little room to measure brand affinity or experience-driven loyalty among new customers. If your marketing is attuned to relationship building, you should be targeting the right customers who will derive long-term value from your brand, and LTV allows you to take full credit for attracting the right customer. More important than getting the full credit for a new customer, however, is the change in perspective that a focus on relationship value will drive. Making lifetime value a component of your KPIs forces employees to think more about the types of customers they want and makes terms like engagement, relevance and brand affinity more than aspirational concepts.
  2. Failing to Measure the Value of Engaging Content. Many companies generate good engagement content, such as brand messaging, product info, newsletters, free apps etc. However, many do not take proper credit for it. Often, marketers treat this content as the first stage in a line of interactions leading to an eventual sales conversion, and it becomes lost in a multitouch attribution model. While sales attribution is important, it is also important to understand if the content fulfilled its immediate purpose. Assume you are an online clothier and you create a style guide to help customers understand versatile ways to wear your product. You’re tracking who downloads the guide and who shares the guide on social media, and then the information is used to segment these customers from those who are potentially less engaged. While this content did not necessarily lead to a direct sale, it did have tremendous value in conveying buying intent, brand affinity or even product preferences. Not all content is designed to drive immediate sales, but it should be designed to drive a specific set of behaviors, which should be measured and valued.

Bear with me as I pontificate for a moment. I am not a believer in over-measuring, but I do believe in purposeful measurement. I believe what you measure reflects the ambition and objective of what you plan to achieve. While not all relationship-focused activities can be easily measured, such as a caring customer interaction, in a digital world the customer’s behavioral response often can. Merely measuring the final behavior of a good relationship — repeat sales — is just too late in the experience journey and that seems to be what most companies are still doing today, despite their desire to build better relationships with their customers.

What I Learned at College: A Business Lesson in Revenue Maximization

Now, after attending three different colleges, I’m impressed with some of the strategies colleges are deploying to make sure they’ve got your kid (and you!) hooked for a four to six year relationship. Some of these institutions have mastered both acquisition and retention efforts and I wouldn’t be surprised if they could teach a course on the subject.

This fall, my twin sons are headed to college. Make no mistake — this is not my first trip to that rodeo. Our older son started his journey six years earlier.

While my husband handled all the college tours, Mom was the designated supporter for completing college applications and, upon acceptance, attending the orientation sessions. Now, after attending three different colleges, I’m impressed with some of the strategies colleges are deploying to make sure they’ve got your kid (and you!) hooked for a four to six year relationship. Some of these institutions have mastered both acquisition and retention efforts and I wouldn’t be surprised if they could teach a course on the subject.

Considering only 55 percent of undergraduates finished their degree within six years, and the average four-year college cost is between $23,410 and $46,272, there are hundreds of thousands of dollars at risk. So, like any good business, colleges have started figure out that there needs to be some really smart marketing strategies at play if they’re going to maximize their student investment, and it involves both the student and a key group of influencers — their parents.

One state college orientation was by and far the most memorable of the three, as I left their event feeling like I was the one going to college (clearly, they had me hooked). So take a few tips and apply them to your own marketing efforts.

  • Relationship Nurturing 101: The Welcome Letter
    You’d think we’d voluntarily joined a membership club with the surprise and delight that exudes from the acceptance letter. “Congratulations,” it chirps, “and welcome to the Class of 2019!” already planting the seed that we’re in it for the next four years. The letter goes on to remind you of all the fabulous things you’ll be encountering on your journey and keeps reiterating that we’ve made a fabulous choice. (Remember, my son hadn’t yet “accepted” their offer, so the sales pitch needed to be a powerful reminder of all the reasons he applied in the first place.) The “handwritten” notation by the Dean of his school of study, casually jotted at the bottom of the letter, added to the personal experience and feeling that they really, really wanted my son to attend.
  • Reaffirming the Purchase Decision: The Acceptance Confirmation Letter
    Once my son had confirmed his attendance, the next communication came via email — and you’d think he had won the lottery. It was lighthearted in tone, oozing with details about what he’ll experience in his campus life, and setting the stage for the mandatory orientation. But instead of feeling like a punishment, it was sold as an exciting way to meet new friends, learn to navigate the campus before classes actually start, and discover “insider’s” tips on how to make the most of your next four years.
  • Onboarding: The Orientation
    A two-day effort, this event was carefully calculated as a way to weed apart the parent/child relationship and start the separate, but equally important, sales pitch(es). Parents registered at a separate table, while students were redirected to a location beyond my line of sight. Parents were directed to a large room where a combination of student leaders and selected faculty sat on the stage. Each one was enthusiastic, knowledgeable and truly made me feel welcome. After brief opening remarks, they asked, through a show of hands, which states families were from – and then encouraged us to introduce ourselves to the people seated around us. Of course that sparked immediate conversation, helped everyone to relax and start to feel an intimate part of a special community. To keep your attention, they broke parents into different groups and moved us into different and smaller rooms for more Q&A-style sessions so that by lunch time, you weren’t worried about finding a buddy to share a lunch table. At cocktail hour, they walked us over to their new state-of-the-art music center where a group of students performed followed by a casual wine and cheese event. The faculty moved easily from table to table distributing a brochure featuring their fall line-up of musicians and it definitely made me want to return regularly to see other performers (yes, I was shifting from “like” to “evangelism” rather rapidly at this point, but they could also see the ROI in that cross-sell effort).It seems my son was getting the same welcoming treatment, only from a different angle. Lots of pretty girls and attractive boys created an upbeat environment. Broken into smaller groups, he was immersed into campus life. They played games, met professors, learned about course options, selected his fall semester classes, played ultimate Frisbee, participated in a water fight, and stayed up late watching horror flicks on the grassy knoll before retiring to his temporary dorm quarters exhausted.
  • Sealing the Deal: The Closing Rally
    At the end of a second day that involved a tour of the athletic center (Olympic-sized pool, five gyms, squash courts, workout rooms, yoga studio – heck, sign me up!), library (where all the cool kids study), and lake-side luncheon (can you say BBQ?), we were ushered into another venue for the closing ceremonies, and that’s the first time I laid eyes on my son since the morning we arrived. He was sitting with his newfound friends wearing a team t-shirt with a big grin on his face. Within minutes of being seated, group by group stood up and shouted their newly created team cheers, razzed other groups, and generally laughed their way to the final gong. Music filled the air as we danced our way out of the venue and made our way back to our cars for the trip home, barely containing our excitement about the start of his new life away from home.
  • Performance Improvement: The Survey
    About 2 days after we returned, we were both sent an online survey about our onboarding experience. Needless to say it got very high marks from this mother and son.

All-in-all, it was one of the best experiences I’ve ever had with any college ever. Everyone was welcoming, friendly, helpful and truly invested in ensuring both me and my son were excited about what the future would hold for both of us. My other two college orientation experiences lacked the genuine enthusiasm, excitement and innovativeness of experience and felt more like their role was to make sure that I realized I needed to let go — but not before understanding that I would not have access to grades unless granted permission by my child.

Many colleges could take a lesson from this state school because I’m sure their retention rate thru graduation is better than the norm. They figured out that it isn’t just the student who needs to fall in love with the school, but the parent, too. After all, my son won’t be writing those tuition checks all by himself.

Sex and the Schoolboy: Predictive Modeling – Who’s Doing It? Who’s Doing it Right?

Forgive the borrowed interest, but predictive modeling is to marketers as sex is to schoolboys. They’re all talking about it, but few are doing it. And among those who are, fewer are doing it right. In customer relationship marketing (CRM), predictive modeling uses data to predict the likelihood of a customer taking a specific action. It’s a three-step process.

Forgive the borrowed interest, but predictive modeling is to marketers as sex is to schoolboys.

They’re all talking about it, but few are doing it. And among those who are, fewer are doing it right.

In customer relationship marketing (CRM), predictive modeling uses data to predict the likelihood of a customer taking a specific action. It’s a three-step process:

1. Examine the characteristics of the customers who took a desired action

2. Compare them against the characteristics of customers who didn’t take that action

3. Determine which characteristics are most predictive of the customer taking the action and the value or degree to which each variable is predictive

Predictive modeling is useful in allocating CRM resources efficiently. If a model predicts that certain customers are less likely respond to a specific offer, then fewer resources can be allocated to those customers, allowing more resources to be allocated to those who are more likely to respond.

Data Inputs
A predictive model will only be as good as the input data that’s used in the modeling process. You need the data that define the dependent variable; that is, the outcome the model is trying to predict (such as response to a particular offer). You’ll also need the data that define the independent variables, or the characteristics that will be predictive of the desired outcome (such as age, income, purchase history, etc.). Attitudinal and behavioral data may also be predictive, such as an expressed interest in weight loss, fitness, healthy eating, etc.

The more variables that are fed into the model at the beginning, the more likely the modeling process will identify relevant predictors. Modeling is an iterative process, and those variables that are not at all predictive will fall out in the early iterations, leaving those that are most predictive for more precise analysis in later iterations. The danger in not having enough independent variables to model is that the resultant model will only explain a portion of the desired outcome.

For example, a predictive model created to determine the factors affecting physician prescribing of a particular brand was inconclusive, because there weren’t enough dependent variables to explain the outcome fully. In a standard regression analysis, the number of RXs written in a specific timeframe was set as the dependent variable. There were only three independent variables available: sales calls, physician samples and direct mail promotions to physicians. And while each of the three variables turned out to have a positive effect on prescriptions written, the “Multiple R” value of the regression equation was high at 0.44, meaning that these variables only explained 44 percent of the variance in RXs. The other 56 percent of the variance is from factors that were not included in the model input.

Sample Size
Larger samples will produce more robust models than smaller ones. Some modelers recommend a minimum data set of 10,000 records, 500 of those with the desired outcome. Others report acceptable results with as few as 100 records with the desired outcome. But in general, size matters.

Regardless, it is important to hold out a validation sample from the modeling process. That allows the model to be applied to the hold-out sample to validate its ability to predict the desired outcome.

Important First Steps

1. Define Your Outcome. What do you want the model to do for your business? Predict likelihood to opt-in? Predict likelihood to respond to a particular offer? Your objective will drive the data set that you need to define the dependent variable. For example, if you’re looking to predict likelihood to respond to a particular offer, you’ll need to have prospects who responded and prospects who didn’t in order to discriminate between them.

2. Gather the Data to Model. This requires tapping into several data sources, including your CRM database, as well as external sources where you can get data appended (see below).

3. Set the Timeframe. Determine the time period for the data you will analyze. For example, if you’re looking to model likelihood to respond, the start and end points for the data should be far enough in the past that you have a sufficient sample of responders and non-responders.

4. Examine Variables Individually. Some variables will not be correlated with the outcome, and these can be eliminated prior to building the model.

Data Sources
Independent variable data
may include

  • In-house database fields
  • Data overlays (demographics, HH income, lifestyle interests, presence of children,
    marital status, etc.) from a data provider such as Experian, Epsilon or Acxiom.

Don’t Try This at Home
While you can do regression analysis in Microsoft Excel, if you’re going to invest a lot of promotion budget in the outcome, you should definitely leave the number crunching to the professionals. Expert modelers know how to analyze modeling results and make adjustments where necessary.

Building Your B-to-B Marketing Database

The single most important tool in B-to-B is, arguably, the marketing database. Without a robust collection of contact information, firmographic and transactional data about customers and prospects, you are at sea when it comes to customer segmentation, analytics and marketing communications of all sorts, whether for acquiring new customers or to expand the value of existing customers. In fact, you might call the database the “recorded history of the customer relationship.” So what goes into a marketing database? Plent 

The single most important tool in B-to-B is, arguably, the marketing database. Without a robust collection of contact information, firmographic and transactional data about customers and prospects, you are at sea when it comes to customer segmentation, analytics and marketing communications of all sorts, whether for acquiring new customers or to expand the value of existing customers. In fact, you might call the database the “recorded history of the customer relationship.” So what goes into a marketing database? Plenty.

First, let’s look at the special characteristics of B-to-B databases, which differ from consumer in several important ways:

  1. In consumer purchasing, the decision-maker and the buyer are usually the same person—a one-man (or, more likely, woman) show. In business buying, there’s an entire cast of characters. In the mix are employees charged with product specification, users of the product and purchasing agents, not to mention the decision-makers who hold final approval over the sale.
  2. B-to-B databases carry data at three levels: the enterprise or parent company; the site, or location, of offices, plants and warehouses; and the multitude of individual contacts within the company.
  3. B-to-B data tends to degrade at the rate of 4 percent to 6 percent per month, so keeping up with changing titles, email addresses, company moves, company name changes-this requires dedicated attention, spadework and resources.
  4. Companies that sell through channel partners will have a mix of customers, from distributors, agents and other business partners, through end-buyers.

Here are the elements you are likely to want to capture and maintain in a B-to-B marketing database.

  • Account name, address
    • Phone, fax, website
  • Contact(s) information
    • Title, function, buying role, email, direct phone
  • Parent company/enterprise link
  • SIC or NAICS
  • Year the company was started
  • Public vs. private
  • Revenue/sales
  • Employee size
  • Credit score
  • Fiscal year
  • Purchase history
  • Purchase preferences
  • Budgets, purchase plans
  • Survey questions (e.g., from market research)
  • Qualification questions (from lead qualification processes)
  • Promotion history (record of outbound and inbound communications)
  • Customer service history
  • Source (where the data came from, and when)
  • Unique identifier (to match and de-duplicate records)

To assemble the data, the place to begin in inside your company. With some sleuthing, you’ll find useful information about customers all over the place. Start with contact records, whether they sit in a CRM system, in Outlook files or even in Rolodexes. But don’t stop there. You also want to pull in transactional history from your operating systems-billing, shipping, credit—and your customer service systems.

Here’s a checklist of internal data sources that you should explore. Gather up every crumb.

  • Sales and marketing contacts
  • Billing systems
  • Credit files
  • Fulfillment systems
  • Customer services systems
  • Web data, from cookies, registrations and social media
  • Inquiry files and referrals

Once these elements are pulled in, matched and de-duplicated, it’s time to consider external data sources. Database marketing companies will sell you data elements that may be missing, most important among these being industry (in the form of SIC or NAICs codes), company size (revenue or number of employees, or both) and title or job function of contacts. Such elements can be appended to your database for pennies apiece.

In some situations, it makes sense to license and import prospect lists, as well. If you are targeting relatively narrow industry verticals, or certain job titles, and especially if you experience long sales cycles, it may be wise to buy prospecting names for multiple use and import them into your database, rather than renting them serially for each prospecting campaign.

After filling in the gaps with data append, the next step is the process of “data discovery.” Essentially this means gathering essential data by hand—or, more accurately, by outbound phone or email contact. This costs a considerable sum, so only perform discovery on the most important accounts, and only collect the data elements that are essential to your marketing success, like title, direct phone number and level of purchasing authority. Some data discovery can be done via LinkedIn and scouring corporate websites, which are likely to provide contact names, titles and email addresses you can use to populate your company records.

Be thorough, be brave, and have fun. And let me know your experiences.

A version of this article appeared in Biznology, the digital marketing blog.

Sales: That’s How Social Media Beats ‘Big Data’

The truth is your target market probably has five big objections you must overcome to win them over—to earn a new customer relationship or grow an account. If you’re a smart marketer (and I know you are) you’ve got to ask yourself: How will this new “big data” trend help me overcome these objections and sell more, more often? Can big data help you get the job done? Should you invest in it with this expectation?

The truth is your target market probably has five big objections you must overcome to win them over—to earn a new customer relationship or grow an account. If you’re a smart marketer (and I know you are) you’ve got to ask yourself: How will this new “big data” trend help me overcome these objections and sell more, more often?

Can big data help you get the job done? Should you invest in it with this expectation?

I’ve been the biggest social media skeptic I know. Yet, I’m living proof: Social media has the power to help a B-to-B brand create leads and sales like few other sales or marketing tools can—even better than big data which, at its core, amounts to educated guessing. I dare say the rush toward big data sometimes feels like another solution looking for a problem!

Social Rocks at Overcoming Objections of Customers
At risk of bursting your big data bubble … your customers likely have the below objections and your butt is on the line to get more prospects past them. Your prospective customer does not:

  • Understand your thing (product or service)
  • Want to value your thing
  • Believe YOU—that your thing will do what you claim
  • Think they can actually DO what you want them to take action on (use your thing to create the needed result)
  • Feel like they can afford it

What’s the common element that could solve the above problem? What’s the killer ingredient that changes everything for your prospect?

Confidence.

Confidence + Trust = Leads
The key to effective B-to-B selling has always been helping customers believe, not in the product, but in themselves—so much that they pull the trigger and buy. Social marketing offers powerful tools to:

  1. create irresistible curiosity in your product or service;
  2. help customers get confident—help them feel like they CAN get what they want, on time, without any heads rolling and even with a sense of joy and accomplishment.

Even more exciting, once customers believe in themselves, they trust the source of that confidence. That source can and should be you.

This is where a social selling expert not only shines but brings home the bacon!

Hire Social Selling Experts
Bottom line: What’s being called big data is a lot of hype, in my opinion. We’ve seen it before in the rise of Enterprise Resource Planning (ERP), for instance. The mad rush into ERP investment has been a huge bust for many businesses. The rush toward big data is dangerously similar. B-to-B marketers have many big data challenges to overcome—from privacy to the idea itself being proven out more substantially.

So you’ve got to ask yourself, “Where should we be investing precious budget dollars?”

If you’re a social media or content marketing believer like me, it’s probably time to do battle with big data. How?

Focus on leads and sales. Hire and/or become a social selling expert. Prove that combining LinkedIn with blogging, Facebook with blogging or YouTube with email can create that needed confidence, break down those barriers to selling stuff and identify precisely when and where customers need help in their lives—better than the unproven idea of big data can!

Realize and take action on how social media enables that critical transference of confidence that helps your target:

  • understand your product or service with total clarity;
  • develop meaningful appreciation for your thing through what you prove to them BEFORE they buy it;
  • trust you—that your product will do what you claim if they buy.

That’s what being a social selling expert is all about. What do you think? Are you a social selling expert?

Introducing ‘The Integrated Email’ Blog by Debra Ellis

Why is email marketing so effective? Is it the one-to-one communication, ability to connect with customers and prospects on the go, or the provision of instant gratification with one-click shopping? The answer depends on the company and the customer relationship, but there is one universal truth: The combination of interactive communication with self-service solutions makes email the most versatile tool in a marketing workshop.

Why is email marketing so effective? Is it the one-to-one communication, ability to connect with customers and prospects on the go, or the provision of instant gratification with one-click shopping? The answer depends on the company and the customer relationship, but there is one universal truth: The combination of interactive communication with self-service solutions makes email the most versatile tool in a marketing workshop.

My experience with email marketing began shortly after Hotmail launched the first Web-based email service in 1996. A client had compiled approximately 11,000 customer email addresses and wondered what we could do with them. Our first test was a 25 percent discount on any order placed that day. A text-only message was sent using the mail merge functionality in Excel and Outlook. It took over two hours to send all the emails.

Those two hours were quite exciting. We had two computers in close proximity so we could watch the progress of the outgoing emails and monitor sales on the website. Within minutes of starting the email transmissions, orders started flowing in. By the end of the day, more than 1900 orders were received. A handful of people asked to be excluded from future mailings. Over 200 people responded with personal notes. Some were grateful for the discount. Others apologized for not placing an order and asked to receive more emails.

Things are much different today. The novelty of receiving a personalized message from a company is long gone. Spam filters make getting emails delivered a near impossible mission. And the competition for recipients’ attention is at an all-time high. Even so, email marketing remains one of most effective marketing and service vehicles available.

The emails that deliver the best return on investment are the ones that are integrated with the other marketing channels to provide information and service to the recipients. They create a connection between company and customer that motivates people to respond. A successful email marketing strategy builds loyalty while increasing sales.

Many email campaigns today are little more than a systematic generation of one promotional email after another. Discount emails are relatively easy to create and deliver sales with each send, making them a quick way to inject some life into lagging sales. The simplicity of sale marketing combined with solid response rates creates an environment where marketers are reluctant to move beyond the easy, low-hanging fruit.

In addition to generating sales, discount marketing also trains people to always look for the best price before buying the company’s products and services. It is not a sustainable strategy because there will always be another company that can offer lower prices and lure customers away. A better plan is to develop an integrated email marketing strategy that educates and encourages people to develop a relationship with the company. This requires more effort, but it delivers loyalty and long-term results.

Every email that a customer or prospect receives is an opportunity for the company to establish itself as the best service provider and solidify the relationship. Best practices include:

  • Using a valid return email address so the recipient can respond with one click.
  • Sending branded emails that identify your company at first glance.
  • Mixing educational emails that provide “how to” information for products and services with new product launches and promotional messages.
  • Transactional emails that communicate shipping information and challenges so customers aren’t left wondering, “Where is my order?”
  • Highly targeted and personalized emails designed to engage customers and prospects at every point in their lifespan.

Finding the right combination of educational, event and promotional emails requires testing and measuring results for incremental improvements. The resources invested improve relationships, increase sales and create a sustainable marketing strategy.

Note: Over the next few months, we’ll feature winning and losing email marketing strategies and campaigns on this blog. If you would like to share your company’s killer emails, send them to Debra at dellis@wilsonellisconsulting.com.

Is Frequency a Pay-off or Piss-off Strategy?

We’ve all heard about contact frequency strategies: Send (often) the same communications to your target audience repeatedly over a period of time. But if you continue to bombard your target over and over and over and over, does it really pay-off? Or does it just piss off your audience?

We’ve all heard about contact frequency strategies: Send (often) the same communications to your target audience repeatedly over a period of time.

The rule of thumb is that you’ll get half of the response rate you got from the prior mailing. So if you got 1 percent the first drop, you’ll get 0.5 percent the second, 0.25 percent the third and so on.

But if you continue to bombard your target over and over and over and over, does it really pay-off? Or does it just piss off your audience?

Earlier this year, I started noticing that Comcast was sending me a lot of direct mail solicitations. And when I say a lot, I mean A LOT.

First it occurred to me that perhaps the marketing team at Comcast had never heard of a merge/purge process. Or perhaps the person who was in charge of merge/purge had gone on vacation … or had been laid off … or had dozed off.

So instead of filing them in the recycle bin like I usually do, I started to save every package that came to my office. And then I noticed that my husband was also being bombarded with the same packages at his home office—so I saved those too.

And then I was personally receiving business mail solicitations at home (my business is at a separate location). AND I was receiving very similar DM packages at home for home service (we already use them for Internet service but not for phone or TV).

While I realize there is no data strategy that will enable Comcast to match me to both my home and business addresses, the fact is, we got over 13 solicitations over a few weeks. THIRTEEN. Some arrived on the same day, while others were a day or two apart. Hello … have you heard of merge/PURGE?

It’s not like it’s a compelling creative package. Pretty plain really. A white, #10 envelope with a teaser in big blue type and my name, in all caps, lasered on the front. And inside? A form letter: No niceties like a salutation—A little “Dear Carolyn” or “Dear Ms. Goodman” would be nice. Nobody bothered to sign the letter. Just, “Sincerely, Comcast Business Services.”

Sometimes the offer changed price-wise (clearly I’m in a test panel), but more often than not, the packages are identical.

Perhaps I’m wrong, but I have trouble believing this strategy has a positive ROI.

Several years ago, a prominent B-to-B client told us that a customer had contacted them after getting 28 direct mail packages in one month from them. Despite being from different divisions, and about different products, it brought home the point: How do companies control the communications flow to any single customer without a proper customer relationship management strategy in place?

I propose that all companies demand that the customer relationship marketing manager job description includes:

Managing and monitoring customer communication to ensure we are never perceived as badgering our customers. That means that no single customer will ever receive more than X no of direct mail or email solicitations in any given 30-day period.

With all of the sophisticated segmentation techniques, it isn’t uncommon that one customer would meet multiple criteria for selection for any given campaign. But part of that strategy should also include the “last time customer received an outbound communication.”

Merge/purge is a lost art. Purge being the operative word here. Finding duplicates. And protecting Customer Zero.

Comcast—I know you’re busy streaming, but are you listening?