Nov. 5, 2020
Effective Marketing Segmentation Strategies to Improve Your Sales with Rob Ristagno
In this episode, host Eric Dickmann interviews Rob Ristagno. Rob is the Founder and CEO of the Sterling Woods Group and focuses his efforts on data analytics and marketing segmentation strategies to spur organic growth. He is passionate about the intersection of data science and sales & marketing leadership.
At the Sterling Woods Group, he and the team help clients reliably grow near-term sales by applying data science to build marketing segmentation strategies, identify your best customers, and build a sales plan that drives more revenue.
He is also the host of his own podcast CEO Campfire Chat which can be found at https://sterlingwoods.com/podcast-ceo-campfire-chat/
Eric Dickmann can be found on Twitter @EDickmann and LinkedIn at https://www.linkedin.com/in/edickmann and my website https://ericdickmann.com
Rob Ristagno can be found online at https://sterlingwoods.com/ on Twitter @Sterlingwoodsgr, and Facebook @Thesterlingwoodsgroup
Episode Summary: The episode summary can be found at https://fiveechelon.com/marketing-segmentation-strategies-sales-s3e6/
If you'd like to contact us with feedback or guest inquiries, please visit:
https://fiveechelon.com/podcast
For more information about Virtual CMO strategic marketing consulting services, visit The Five Echelon Group at https://fiveechelon.com
Episode #37
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WEBVTT
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The Virtual CMO podcast is sponsored by the strategic marketing consulting services of The Five Echelon Group.
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If you’d like to work directly with The Five Echelon Group and receive personal coaching and support to optimize your business, enhance your marketing effectiveness and grow your revenue, visit Five Echelon.com to learn more and schedule a free consultation.
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Welcome to The Virtual CMO podcast.
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I'm your host, Eric Dickmann.
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In this podcast, we have conversations with marketing professionals who share the strategies, tactics, and mindset you can use to improve the effectiveness of your marketing activities and grow your business.
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This week, I'm excited to welcome Rob Ristagno to the podcast.
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Rob is the CEO and founder of Sterling woods group.
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And he had previously served as a senior executive at several private equity owned businesses.
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He began his career at McKinsey and company, and has held various roles at major organizations, including Visa, Pepsi and Comcast rubs.
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Focus has always been on embracing digital technology and data science to spurs strategic growth at Sterling woods.
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He and his team are passionate about helping clients grow their sales organically by applying data science.
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Rob, thank you very much for joining the virtual CMO podcast.
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I'm so glad you could be with us today.
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It's a pleasure.
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Good to be here.
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So I wanted to start, just get a little bit of background on you.
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I know that you've worked at McKinsey and Visa and Pepsi, Comcast America's Test Kitchen.
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What made you found Sterling Woods?
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In my whole life, I wanted to start my own business.
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And the advice I got was simple advice.
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Just look for a problem to solve in an area that you're passionate about.
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And I'm really excited about it as a marketing.
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And I'm really excited about data science.
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So the intersection of those two things are my passion.
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And I worked for various companies.
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I started my career with the big companies as you listed off, but then I worked for a series of mid cap, medium sized businesses and realize that the problem that exists there.
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Is that there isn't necessarily the budget or bandwidth to do a lot of cool things, that I was able to do at the bigger companies with using data to inform your sales and marketing strategies and actions.
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So I said, wouldn't it be great to find a way to make this accessible.
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And because I always enjoyed when you have these insights from big data, it makes her job easier.
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You know what I mean?
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It's, you feel like you're working on the right stuff and stuff works more often than not.
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And it's a great feeling.
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He could spend more time being creative and thinking of new ideas and say, well, why not solve this problem for medium sized businesses?
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Who have a bunch of data sitting around, be fun to help them make sense of it and improve their sales and marketing actions.
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So I love that as you and I discussed previously, I come from a background of customer relationship management, having worked for years at Siebel Systems and then Oracle.
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So that's near and dear to my heart.
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When you go into a lot of these businesses, do you find that they have a good repository of data?
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It's something that usually.
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concerns people either from one of two reasons, either they think they have no data or yeah, we have data, but it's on max hard drive or Susie's a Excel spreadsheet or something like that.
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So that's why, we encourage people to be very hypothesis driven and how they use their data.
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So you can go and like a Viper to get exactly the data sources you need.
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And so you can ask questions of the data.
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A and B purpose, purposeful hole about that.
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We also find that companies have a lot more data than they realize.
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In fact, a big nugget is, your sales history data.
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So you have, presumably if you're in business, you have transactional data, you have transactional history.
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And that's a great place to start.
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And the other thing is you can always go and collect more data.
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You can always go out and run surveys of your customer base or prospect base.
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you can go do some interviews.
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So there's ways to compliment whatever data you have with primary research.
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That's very interesting that businesses think that they have too little, but when you actually get in there, you find that they actually do have quite a bit, but as you said, it's spread around.
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It's typically not in one system, like a CRM system it's buried in spreadsheets or old sales systems So it was one of the things that you do try to pull all that data together.
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Yeah, we recommend a one, two punch.
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The first fund is let's get some value out of the data.
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So instead of star, you.
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Theoretically.
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You're supposed to have a unified customer database or data warehouse or data Lake, whatever phrase you wanna use.
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And those projects I've seen, unfortunately, cost millions of dollars and take years of people's time.
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And then you get to the end of the process and you're like, so what.
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Yeah.
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What are we gonna do with all this awesome data?
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I'm a fan of organizing your data and getting a unified view of the customer and building proper data warehouses.
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But start with proven the value of your data first.
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So start by plucking data points from here and there.
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Get the organization comfortable with using data, making better decisions that are grounded in facts.
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And then when you get some momentum there and can prove to your boss or your shareholders or colleagues that this is something that has some legs, then you can make the pitch for investing and doing it the sophisticated way with data warehouse.
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When businesses have data or they start to look at their data.
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Do you find that often?
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They're not exactly sure what to measure or how to measure it or that maybe they just don't have any analytical tools to be able to take the data and put it into some format that you can actually read and make decisions on it?
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Yeah, I think a couple of common pitfalls we see is one, a lot of people look at what I would call business intelligence about the past.
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So a lot of people have a dashboard report or a monthly management, our weekly management report.
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Unfortunately, and I've been guilty of this early in my career.
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There are 12 pages long and so detailed and have so many different data points.
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They become useless again.
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I think that's one thing is that the tons of data points about the past is it isn't necessarily something that's going to help you grow your business.
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I think that the pivot, the change that we see businesses making now, is taking about how you can use your data to make predictions about the future.
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And there's lots of statistical techniques here you can use.
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They're actually the secret of big data as these.
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These are the same statistical methods I learned about in college 20, 25 years ago.
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but now they can be more powerful and more automated and crunch bigger datasets.
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But for the fellow nerds on the audience, stuff like K means clustering, a decision trees, survival analysis.
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Next five to buy modeling.
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There's all of these predictive models that you can build, and the statistics behind them.
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Yeah, 50 years old, probably plus, but now we have the opportunity to technology is cheaper and faster and bigger.
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so that's really where, the opportunity comes.
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How do you deal with the biases that come in with looking at the data?
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And what I mean by that is maybe you work with an organization that's a sales focused organization.
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So everything has the hat of sales on it, or to marketing focused organization where everything is about, lead generation and conversions or maybe it's a technical, focused organization or operations focused where they're not even thinking so much about marketing and sales.
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whatever their product is, we'll.
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Sell itself.
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How do you sort of deal with those different biases when it comes to looking at data?
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Yeah, I think, Eric.
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You have your finger on a very important organizational point and how we solve that with the companies we work with, is really focused everything on the customer's, the hero.
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So all data starts with data about the customer.
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And then it's tough to argue that this is a data center.
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Marketing centric or an operation centric, or sales centric, point of view.
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If everything is, Hey, look, we're splitting the data Switzerland here.
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This is what the data's telling us that our customers like and want and do then it's then I think you can come to the table with your colleagues and other functions and have a productive discussion about the so what is for your department.
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So you can do everything together as a team.
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That's in the best interest of your customers, which of course is ultimately the goal of any organization.
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I love that because it takes it outside the organization as well, and really focuses on the end game.
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Which is making or getting new customers and making them happy and satisfied.
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So when you start to look at this, so you've got all this data and ultimately you want to build some sort of a segmentation strategy, right?
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So what are the elements that you really need to pull together as you start to look at building out a segmentation strategy?
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There's two main things that we see a lot of power in.
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Traditionally, when you think of segmentation, a lot of people have done a segmentation along demographic, our firmographic lines, for example age or income or industry or role within an, within a company and there's some value there too.
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But the real power comes from two new types of segmentation or two types of segmentation that are easier to do now.
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and the age of big data, one is a behavioral segmentation.
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So any data you have in your CRM or your email marketing system, or if you have Google analytics or any sort of web event data, just what is that journey to buy look like and how does it vary by customer type and how can you therefore put the right messages in front of the right people at the right time?
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Either automated online.
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Or through three reminders to your Salesforce.
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Hey, it looks like Eric.
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Did these three things and people who did those three things in the past for 90% likely to buy.
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So give him a call.
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so that's one is behavioral and two is psychographic segmentation, and this is a type of.
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This is segmenting your customer base; grounded in their attitudes and their beliefs and their emotional attachment to your product, your industry, your brand.
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and this has been thought of in the past, and usually in the past, it's been, thought of more of a brand marketing thing where it's something you can ask people questions in a survey or be focus groups and build, build these attitudinal segments, but they weren't really actionable.
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So you can help when you're buying media and you're trying to get the right messaging across.
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It's helpful.
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But now with big data and with, especially with content and content marketing, you can actually make these attitudinal segments actionable and you can pull them into your direct marketing and direct sales efforts.
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Because, based on, what people are consuming on your website, what they're saying on sales calls, you can track all that data and then put people into the right attitudinal segment so you can offer again.
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Put them through the process.
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They want to get through, talk about the right features and benefits of your product based on what segment they're in.
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I figured out what is the right price point for them to be in depending on what their attitudes are toward your product.
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And you can really go a long way.
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In fact, we find the biggest lift from attitudinal segmentation, which this is something that even just 10, 15 years ago, it's very difficult to implement in the real world.
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So ultimately the segmentation is helping you determine who are your target customers and within that group, who are your best customers, right?
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The people who have already bought from you in what are the trends there?
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Exactly.
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So for each of these segments, if you look at the three things, the cost to acquire the customer, the average annual revenue for that customer and then the customer lifetime value.
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You can clearly see what you just mentioned is these are high value segments.
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These are low value segments and the beauty there's two things that are beautiful about that.
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One is you can stop wasting sales and marketing time and money against the low value segments and put more of that money toward the high value segments.
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Get a bigger bang for your buck both on the sales and marketing side.
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But then you also know exactly what makes those people tick.
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So even within your focusing on your very best customers, we'd like to call them your whales.
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You can give them the exact right messaging, the exact right product.
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They exact right sales process that they need.
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so it's really helps you be more efficient with your resources overall, but then when you are targeting a specific segment, you know exactly what they want.
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What are the tools that you bring together to develop this segmentation strategy?
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And I know you've got something, I believe you call scout, right?
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Is that a tool or is that a process?
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Yeah.
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It's a par process part tool.
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But the clients don't have to worry.
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we operate the tool.
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It's just a tool to make us our process faster, more efficient and more accurate.
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So that's part of the problem with a lot of these segmentation projects.
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In my early days at McKinsey, it would take us months to get a lot of these things done because we were reinventing the wheel for every client.
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And so we're trying to take the best of both worlds here, where we know the mechanics, of course, every client's data and recommendations or are we spoke to the client.
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But the actual process we go through has been automated.
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So we can now build a segmentation and hours or days rather than weeks or months.
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but yeah, so I think it's a way to spend less time crunching the numbers and more time figuring out what to do about what the numbers say.
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And you can't really talk about things like big data and analysis without throwing AI in there somewhere.
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How do you see AI as really changing the ability for marketers and business executives and sales personnel to really understand these segmented groups and how to most effectively market and sell to them?
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I think it comes back to, giving people exactly what they want.
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And that's where I see as the role of marketers and sales professionals.
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Using artificial intelligence to help me very quickly understand this person before I even speak with them.
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So I know exactly how to treat them and I want to treat them the way that they want to be treated.
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I was say the golden rule is treat others how you want to be treated, but the platinum rule is treat others how they want to be treated.
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And so AI helps you, uphold that platinum, the platinum rule there.
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I will say one, one thing that we found that's important is that.
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AI and maybe your next question was going to be, what about machine learning?
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these are hot topics right now and definitely critical and important, but I will say don't forget, don't ignore qualitative things of understanding your customer.
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Sometimes you get false positives and the data.
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There's a correlation with causation.
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So we really encourage our clients and we help them with it.
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do a lot of interviewing of customers to make sure you understand the reasons why the models are telling you what they're telling you.
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I think you'll be an even better marketer or sales professional.
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If you can look at what the data's telling you, see what the machine learning is saying, see what the AI is saying, but then go and actually knock on doors and talk to your customers and help they'll help you bring it to life.
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And you'll be even more effective that way.
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I would add to the platinum rules.
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If we're going to put a plaque on the wall and put all these rules up, that it's always more cost efficient to sell to your best customers then to go out and to try to find brand new leads in the marketplace.
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So when you're dealing with these businesses and helping them understand their data and their segmentation, do you get the feeling that many of them truly understand the lifetime value of their best customers?
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Unfortunately, not the biggest problem we usually see is people assume their biggest customers are their best customers, biggest from a revenue standpoint.
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And then sometimes they are, but often we find that the biggest customers they're there because they negotiated a great deal with you.
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And as soon as the contract's over, they're going to bid it out again and go to the lowest bidder again.
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Sometimes they're more demanding.
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This is not an on all the cases of course, but in some cases their cost of service so high and your margins are so low.
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Sometimes we even see them being unprofitable, which is a, which is really tough.
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so yeah, almost I think I can knock on wood and say 100% of the time so far, We found that people are not necessarily focusing on their highest value customers as well as they think they are.
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That there's often a surprise in the data hidden nugget of gold.
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In the data.
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So to speak that, Hey, there's this actually, there's a segment, just a little bit to the right of what we think are our best segment is that's way better.
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And also the flip side that sometimes there's a segment out there that because of, I don't know, inertia in the organization that people think in the boardroom is a very good segment, but we actually find is one of the worst segments.
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And even just stopping, serving that segment or changing how you're serving him, making them pay for the extra services they're getting or change the pricing or cut some features.
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it can go a long way just making those small tweaks.
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I love that point because I see it so often that businesses go right into a headwind where they're going head to head with a competitor.
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Competing on exactly the same playing field when really all they need to do is move a little bit to the left or a little bit of the right to find that clear space.
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And then they have a real opportunity to compete because going head to head, especially with the larger competitor can be very difficult.
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But sometimes if you find an area of weakness, That's where you should be spending your money, but there's sometimes real resistance in the boardroom to doing that.
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Yeah, no, you're right.
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You're right, Eric.
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And I couldn't agree with that point more.
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And hopefully when we come to the table with data and convinces the skeptical board members, that this is not actually a risky thing of most of our clients are owned by private equity businesses, and in the private equity space.
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We hate risk.
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We want 20, 30%.
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IRR is almost guaranteed.
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We, and we're on a time clock and we have five years to execute our plan.
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And we don't like a lot of risk.
00:16:32.490 --> 00:16:37.650
So we don't like making huge, risky bets and trying to go what might be perceived as outside our sweet spot.
00:16:37.740 --> 00:16:44.191
So that's the power of data as it helps you nudge outside of that sweet spot, so you can find opportunities and you find that there's actually easier and easier.
00:16:44.191 --> 00:16:47.640
if you're doing exactly what the data tells you, it's a way to de risk what you're doing.
00:16:48.390 --> 00:16:55.240
Now, when we talk about things like McKinsey and board rooms we can certainly leave the impression that this is an exercise for larger companies.
00:16:55.451 --> 00:17:07.131
But in fact, if you're a small company, even though you might not have access to all the same tools, Analyzing your data, understanding the segmentation who are your most valuable customers are it's critical to your business as well.
00:17:07.550 --> 00:17:08.300
Yeah, absolutely.
00:17:08.300 --> 00:17:19.550
Even if you're going to start up a company from scratch with no data, you could at least make some educated guesses as to who you think your whales are, your best segments are, and you can run a bunch of experiments to learn over time.
00:17:19.580 --> 00:17:22.340
As long as you're tracking everything, you can see what works and what doesn't work.
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And correct course.
00:17:23.931 --> 00:17:24.411
accordingly.
00:17:25.010 --> 00:17:30.201
So when you do the segmentation and then you go back and apply it to a company's marketing efforts.
00:17:30.335 --> 00:17:39.964
Do you often find that what's going on is they are casting far too wide, a net, and really what the segmentation now allows you to do is be much more focused in your efforts, which.
00:17:40.505 --> 00:17:42.515
Ultimately can bring down your costs, right?
00:17:42.904 --> 00:17:43.055
Yeah.
00:17:43.115 --> 00:17:43.295
Yeah.
00:17:43.355 --> 00:17:47.775
So for, yeah, for one example that our client is at leadership training space.
00:17:48.345 --> 00:17:54.105
And they were doing a lot of what I would call general brand advertising before they decided to engage us.
00:17:54.315 --> 00:17:59.684
So a lot of beautiful well-designed adds clever ads, but casting a very wide net.
00:17:59.684 --> 00:18:02.684
And as we know, not all CEO's are created equal.
00:18:02.805 --> 00:18:11.194
You have solo preneurs who are managing anyone, and then you have the, Tim cook or so on the CEO of Apple and a lot of shades of gray in between.
00:18:11.704 --> 00:18:18.184
What we found is actually for types of CEO's that were great fits for the service, this professional development service.
00:18:18.464 --> 00:18:21.634
So just to bring them to life, one was more we call them, a micromanager.
00:18:21.664 --> 00:18:33.664
so the owner operator, usually who's hands on the business and they felt like they were getting no leverage and no time and burned out and work life balance was tough, but they had a hard time letting go and trusting people on their team.
00:18:34.115 --> 00:18:36.694
That was one segment that benefited a lot from the service.
00:18:37.035 --> 00:18:39.704
Another one, just to contrast It is the peak performer.
00:18:40.075 --> 00:18:49.015
The person who gets up at four in the morning to do yoga and drink shakes and crushes their numbers and goes from company to company and is very competitive and loves to win.
00:18:49.644 --> 00:18:51.865
You can imagine those, both those two segments.
00:18:51.865 --> 00:18:52.464
We're great.
00:18:52.734 --> 00:19:05.484
We're great customers, low, relatively low acquisition costs, relatively high customer lifetime value, but the message you would use to attract those people, both from a content marketing and then from a sales pitch perspective is totally different.
00:19:05.634 --> 00:19:14.384
So just understanding that those two segments exist really helped and this company in particular was able to lower their acquisition costs by over 75%.
00:19:14.474 --> 00:19:22.684
I think it was 78% reduction in acquisition cost as a result of having a much more targeted message rather than broad brand generic messaging strategy.
00:19:23.315 --> 00:19:25.055
That's a significant change.
00:19:25.085 --> 00:19:33.615
And is part of that, you also increase the focus on developing organic leads, as opposed to having to spend all that money on paid advertising.
00:19:34.015 --> 00:19:34.285
Yeah.
00:19:34.285 --> 00:19:34.434
Yeah.
00:19:34.494 --> 00:19:41.984
I think the, the way I view it's like a boat where the paid advertising is the engine that the motor or whatever that gets you going a little bit, but you really want to be building sales.
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so that the wind does all the work for you.
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