The Critical Path Ahead for Tech CMOs

It’s here.  The 21st CMO Survey is out and it gives us the opportunity to lift our heads up from our day-to-day work with our Technology clients and understand CMOs’ perspectives on where they are placing bets on growth and where the greatest risks of failure in their operations might be.  It’s a good tool for us because it helps direct our focus on what the Go-to-Market priorities must be for our clients for the coming year.

Here are the top-four takeaways from the survey for the Technology industry:

#1: Growth from Existing Markets Dominates Spending…and Requires Better Cross-Sell Analytics

Technology CMOs allocate three-quarters (74%) of their total marketing spend on their top priority: Driving growth from existing markets.  Within this spend, an increased and continuous focus on account cross-sell and retention activities was critical. These are areas where predictive analytics can be especially powerful in helping to identify, target and prioritize white space marketing campaigns, next-logical product plays, and new buyer acquisition within the existing client base.

There’s good news here for Tech firms; you have unique data sets versus many other industries.  Your growing access to proprietary, IoT-based product telemetry data—plus usage data sourced from Cloud-based services—can hold valuable insights.  In 2018, the number of IoT connected devices worldwide is expected to reach 1.2 billion.  That’s a lot of potential customer data!  Sourcing and finding patterns in that data, especially when combined with additional triggers on marketing engagement and online buying signals is really most of what you require to secure a go-to-market advantage through customer analytics.

Get more detail on cross-sell techniques leveraging IoT data

#2. Digital Starts to Dominate Marketing Activities, but Also Exposes Major Skill Gaps

Marketing budgets for Tech firms grew by 8.6% over the past 12 months, and are expected to tick up slightly to 10% growth for next year.  But within that budget, a major strategic shift continues to play out.  Spending growth on digital marketing efforts outstripped all other budget areas at a 13.6% YoY growth rate.  With 53% of budget already allocated to digital marketing activities, and an expectation that will grow to 62% over the next five years, that shift in spending mix will create more data and greater CEO expectations for a bullet-proof case for empirical, analytics-based allocation of budgets to only the highest ROI programs.

But Tech marketers are playing catch up.  35% of Tech CMOs feel they are below average at developing strong knowledge and skills for learning what works and doesn’t work for digital marketing.  That’s likely why building up Marketing Technology skills is a top priority (37% indicate as a top priority skill to hire for) especially with the expected growth in digital campaign spending.

See how Microsoft, Adobe, and SAP are reacting to pressure for better marketing analytics with their Open Data Initiative

#3. Tech Marketers Struggle to Integrate Customer Data and Analytics

Despite the explosion of AI, Predictive Analytics, and Big Data, survey results suggest that spending on analytics as a % of total marketing budget has not changed significantly over the last 6 years (Tech respondents averaged 7.3% in the August 2018 survey).  Marketers appear to be behind on tapping into this reserve of potential value.

In fact, only 4 in 10 marketing projects use analytics before approving investment and 1/3 of Tech respondents believe that marketing analytics contributed to their performance “not at all” or only slightly.  It’s very unlikely that analytics fails to offer value…and much more likely that CMO’s teams lack the knowledge or skills to tap into valuable insights.

See how leaders are instrumenting marketing analytics efforts to measure “Return on Marketing Analytics”

As an example, over 50% of Tech respondents rated their ability to integrate customer information effectively into marketing programs below average.  Worse, only 3% of respondents felt they were “very effectively” doing this.  It’s an understatement to say there’s room for improvement.

Most Tech CMOs do appear to realize that they must close this gap.  Almost 50% believe that they lack people who can link analytics to marketing practices.  As such, future hiring needs around Data Science is the #1 competency gap identified — 37% indicated it’s their primary or second most important need, which is 10 points higher than the average across all respondents (Tech and non-Tech).

#4. Yes, Innovation is Your Responsibility

Marketing budgets at most tech companies are a sizable business expense, 9.7% of Tech firms’ revenues on average.  That’s $97 million for every $1 billion in revenue, so expectations are high for ROI.

On top of that, constant change seems to be the only constant in this industry.  41% believe their company’s marketing strategy will be substantially different in five years, and yet less than one-in-five Tech CMO’s indicated that “Innovation” was a primary responsibly of marketing.  Three-quarters of respondents already feel pressure from their CEO or Board to prove the value of marketing, and almost 60% see that pressure increasing.

If Marketers don’t lead that substantial strategic change, mainly with deeper customer analytics, stronger skill sets based on best practices, and advanced digital marketing activities, the CEO may be finding someone else to deliver that innovation.

In summary, Technology CMOs, like their peers in most industries surveyed, expect to increase their investments—but they must do so not just in technology tools, but in repeatable processes and skill sets that truly deliver better go-to-market innovation and performance.

What lies ahead for Tech marketers is an exciting path where generating more and more intelligence on customer needs and more quickly engaging buyers with go-to-market programs can deliver exceptional marketing-fueled growth…and the ROI to not only defend, but to grow budgets year-over-year.

How to Build a Product-Centric Data Science Organization

Realistically, most data science is heads-down, unpredictable activity. Typically, a data scientist is given an objective, such as “tell me the part-worth of TV in my advertising mix”, or “come up with a classifier to put a lead into the correct segment,” and has to figure out how to solve the problem with a vast array of tools and potential data sources. This might take hours or days, but the iterations and deep thought required to get to an answer are significant and differ significantly (at least in my experience) from task to task.

What am I driving at? Data science is at its core an individual voyage of discovery. Big-S science provides frameworks around which to structure this voyage of discovery— you know, the old “hypothesis / background / procedure / results / discussion” framework that we were all taught in high school chemistry. But ultimately, inside of “procedure,” there are hours and hours of arms “deep-in-the-data,” pounding away in StackOverflow that is really hard to codify.

This is a tough challenge for organizations because, in my experience, organizations don’t function well or scale relying on the “individual hero” or “craftsman” model.

To scale, organizations need to productize—that is, find common approaches, algorithms, methods, data structures—to solve common problems.

These common approaches can be configurable. Yet ultimately, a product approach needs to scale, improve itself, and be reused by others.

I’m not saying that data scientists need to abandon all creativity and individuality. I do think that truly scalable data science organizations are possible. They will ultimately make everyone, including the super smart creative data scientist, happier in their job. There will be less reinventing the wheel, less manual work, and a better understanding of value provided across the organization.

Fortunately, a lot of the infrastructure and best practices for scalability already exist. We can borrow the best parts of the software development lifecycle, specifically the Agile methodology, to evolve into a “product-centric” data science organization. I’ve had success building this kind of organization, and below are my nine best practices that work, along with some specific tools, frameworks, and processes that go along with them.

1) Implement Version Control

It still surprises me how many data science organizations don’t use version control. Whatever you’re using—Git, BitBucket, etc.—the idea that code is sitting around in C:\ drives or on some Sharepoint site or whatever, YET not tracked, is low hanging fruit. Every data scientist should not only be using version control but should have a branching strategy: Don’t commit to master! Do have a coherent naming convention! Do add commit messages that describe what you did!

2) Separate Projects from Libraries.

Data scientists should do individualistic heads-down work but, they also need to be trained to notice when their work has gone from a one-off to a reusable format, and then transition that to a library (or package). Libraries or packages have different requirements when it comes to documentation (i.e. readme files), parameterization, and general code elegance. To help understand when a “project” is turning into a “product,” code reviews are a great help.

3) Implement Reproducibility

A data science organization should avoid, at all costs, producing reports, PowerPoints, dashboards, etc. that were created by dragging, dropping, and clicking. Instead, they should invest the extra time in writing that PowerPoint programmatically. You can do this either using officeR in R, or python-pptx, or in creating that dashboard coded with a tool like Shiny. If you need a report generated periodically, build it in something like RMarkdown or knitpy. Or, just send someone to the Jupyter Notebook. This will pay dividends both when someone asks you literally “how did you get that number” or you ever want to reuse anything you just built.

4) Implement Agile Project / Product Management

If I had to pick, this might be the most important best practice. There are many aspects to Agile, but the concept of the sprint, backlog, and strategic priorities, arrayed on some kind of a shared board, really helps data science teams work. I like a less structured tool like Trello better than a more structured tool like Jira for data science. This way, lists can evolve flexibly and sprint can be less rigorously defined. If a data science team is split up between a bunch of projects, those can be separate boards or lists, depending on how you want to roll. What matters is that everyone can see clearly what everyone else is working on along with what is up next for a longer-term picture. Writing good functional requirements on each card / ticket (as Trello calls it) is an art in and of itself. While it shares some things in common with software, writing up tasks/stories for data science has its own unique tips and tricks (out-of-scope of this blog).

5) Write Down Procedures

Duh, right? But this is often missed. Every data scientist might have their own way of doing things, and this is fine within reason, but ultimately, procedures, environments, security protocols, etc. all need to be written down. I’ve had great success leaving these as markdown readme’s on Github, but as long as there’s a single source of truth, and people know where to find it, you’re good.

6) Have Code Style Guidelines

It’s not essential, but standardizing code can help a lot in productization. For example: Are comments on separate lines or to the right of code? What is the right level of commenting? How important is it that data scientists make code Pythonic? Should we put helpers functions into one file, or split them up (and at what point)? This might be something that evolves over time, as lead data scientists develop a point-of-view on this that is evidence-based and not just based on personal preference.

7) Have Standups and Demos

Again, basic Agile stuff, but be sure to have your data science team get together in the morning to go around the horn, talk about what they’re doing today (any blockers they may have), and just generally keep on the same page. I’ve had people who push for this to be a “just the facts” meeting, and I get that, but I personally err on letting people talk. Ideas are created, people are cross-pollinated, and ultimately a few extra minutes of talking leads to non-linear gains in productivity in my opinion.

8) Have Standard Data Definitions

If you’re dealing with the same data structures over and over, don’t let every data scientist have their own way of describing the data. Using an example from the sales and marketing world, if we’re constantly looking at opportunities, take the time to define an XML (or flat) definition of an opportunity. Leave it on your version control (in the libraries section) and reuse it. Take the time to have your database or developers write an endpoint to represent it, and use it in all your code. In the long run, parameterizing your variables and making them product-ready. Important: Don’t write different data definitions for every different system. Go spend a couple hours and write an adaptor to your standard definition that can work with various systems so others are able to figure it out.

9) Enlist the Data Team.

The folks that are responsible for building your data warehouse/lake should also get in on the fun. While I find that database developers tend to do their own thing, all of the SQL code they are writing should be in the same version control system, and it’s helpful to cross-pollinate with the data scientists. A lot of times, huge light bulbs go off when the data scientists tell the database folks why they need a certain view. Conversely, data scientists who grumble about latency or speed might see the light when they hear the database engineer’s side of the story.

There are probably many more best practices I could share, but listed above are the most low-hanging fruit. At MarketBridge, we have the added layer of essentially exposing these best practices to our clients, making them a part of our product-centric data science team. It’s how we make sure that our results are actionable and reproducible. It’s also how we get prototypes from a data science-generated idea into a formalized product. That’s a topic for another time.

Earn Your Seat in the C-Suite: 6 Steps to Actionable Program Reporting

Do you find yourself with an endless supply of reports but unable to get answers to your most critical business questions? Don’t worry – you’re not alone. Many companies are faced with the same exact issues that you are. It’s very likely these companies have one or more of the following foundational problems:

  • They don’t tie business results back to objectives or measure against goals
  • They lack a cohesive strategy to measure their efforts across the business
  • They tend to build reports from the ground up and overload their end-users with pages of data and metrics

Do you notice a theme here? It boils down to a lack of planning in report development. While many of these companies may have large data warehouses with endless amounts of information, they end up with numerous metric-heavy reports that don’t answer any specific questions. This is great for keeping report developers busy, but it is terrible for driving the business forward based on actionable insights.

So how do you fix these issues and avoid these pitfalls? Instead of diving right in and building another report, I’m going to recommend that you take a step back to outline a measurement framework using the following steps:

  1. Identify Business Objectives
  2. Determine KPIs
  3. Define KPI Goals
  4. Identify Supporting Metrics
  5. Determine Reporting Dimensionality
  6. Organize into a Framework

This process will help to provide structure to business questions. Using your framework as a base, better reporting and answers to your questions will follow. We’ll cover the first 3 steps today and finish it up in a future blog post, and wrap up the series with a comprehensive whitepaper which will include examples. Stay tuned!

1. Identify Business Objectives

The foundation of great reporting is built through first asking the following question – What business goals are we trying to achieve? It sounds really simple, but this question is often overlooked and left unanswered. By asking this basic question upfront, you can better define the objectives and scope upon which the reporting will be built.

2. Determine KPIs

After the main business objectives are defined, we need to determine how to measure success for each objective by defining key performance indicators, or KPIs, for each one. A KPI is the most important metric to measure for each objective. As reports are built, these will be the primary metrics that senior leadership should ask for and the ultimate measures that progress should be measured against.

3. Define KPI Goals

If you don’t have a goal against which to measure your KPIs, the results become completely arbitrary. I previously worked with a company that understood the first two points but skipped this important step. When it was time to report on the results of a promotion, it was easy for a campaign manager to pick out pieces of data from the results to shape his story into a success. He would tell management that the company earned $100,000 in revenue from a promotion without sharing that a similar campaign from the previous year earned $500,000 or that the campaign cost $1,000,000 in resources and discount expenses to run. The point is that you should define specific goals for each of your KPIs. This way, it is easy to measure performance against specified benchmarks when delivering results to management.

Goals for individual KPIs should be seriously considered prior to developing reporting. These benchmarks should not be set in stone; they can change over time or vary for individual campaigns. The purpose of these goals is to put into context each KPI so that you can determine if performance is better or worse than the target.

Part 1 Conclusion

We have laid the groundwork for an excellent reporting framework by outlining our business objectives, identifying KPIs that align to those objectives, and defining the goals to measure success. In the next blog post, we’ll continue to build our measurement framework outlining through the 3 remaining steps. You’ll also have the opportunity to download a free whitepaper on this subject. Then, you’ll have the keys necessary to develop your own frameworks that will be the foundation for better reports. Stay tuned!

“Marketing Leads are Crap!” How to Achieve Sales & Marketing Utopia

6 Questions to Ask your Sales Team to Get Buy-In Between Marketing and Sales

You’ve crafted a perfect user experience that guides your prospects through the buyer journey and nudges them to perform certain behaviors that will deem them as ready to be put in contact with a sales person. Maybe you’ve even set up your processes correctly to automatically route leads from marketing automation software to the CRM. But after leads are sent to sales, the ball is out of your court and the sales reps will just automatically pick up where you left that lead in the buyer journey, right? Wrong. If you haven’t aligned with your sales team, those shiny new leads most likely come to a screeching halt as soon as they’re passed to sales. In fact, 79% of marketing leads never convert into sales (source: MarketingSherpa). But why?

Perhaps it is because the sales team doesn’t know they’re coming, and don’t have time to reach out to them quickly. Maybe they have a preconceived notion that marketing leads aren’t qualified, so when they come through, they deprioritize marketing leads, burying them in a list titled “To Follow Up.” Regardless of how much research and planning you do to craft a perfect demand generation or nurture program, if you don’t have buy-in from the sales team in your organization, you are leaving a gaping hole in the buyer journey.

In order to close that gap, keep sales involved both before and after your campaign, by asking them these 6 questions:

Questions Sales & Marketing Should Discuss During the campaign strategy and planning phase:

1. Who are your target customers?
Talking to your sales team about your target market is important because they are the ones on the front line, having intimate conversations with buyers. While marketers are able to bring a holistic, “big-picture” perspective of your target customers, sales people interact with them one-on-one, and know them on a more granular level. Customers expect a user experience fully customized to their needs now more than ever, and so it is extremely important to ask them to share their understanding of these lead on a detailed level.

2. What are their biggest objections?
Your sales people hear these objections every day, and when dealing with every-more savvy consumers, understanding your target consumer’s biggest objections to your product or service will help you to craft your camping messaging and assure that you are addressing them.

3. What information did you wish you had about a lead coming in?
While there are many factors shaping your form strategy, you should talk to your sales team to understand which key pieces of information dictate the route their conversation takes. Go beyond the standard demographic information and ask about fields that may be specific to that product, service, or industry that will give the sales person a much better idea of what to expect when picking up the phone to call this lead.

4. What do you consider to be a sales qualified lead (SQL)?
Sales and Marketing need to be in agreement as to what actions or behaviors indicate that a lead is primed to talk to a sales rep. If you take their opinion into consideration when defining an SQL for this campaign, they’ll be more excited about receiving leads from marketing and follow up with those leads faster.

Once you’ve decided on a definition for an SQL, be sure to also communicate during the campaign what route a lead took to get to them. Did they raise their hand and specifically ask to talk to a sales rep, or did they download 2 whitepapers and attend an event? Arming the sales person with this information will give them context and allow them to start the conversation accordingly.

5. How quickly do you follow up with a lead after they are assigned to you?
Best practices say that leads who are expressing explicit interest should always be followed up with as soon as possible (in fact according to InsideSales’ 2014 Lead Response Report , 50% of buyers choose the vendor that responds first) however whether it be due to human or technological constraints, an immediate response is not always the standard. Understand what is feasible, and come to an agreement on a SLA (Service level agreement) for how soon the sales team will follow up with leads, and how long they’ll pursue them for. That way, in planning your campaign, you can ensure to set expectations with a prospect on when they should expect to hear from a sales person should they request to speak to a rep or would like more information.

Questions During and after your campaign:

6. What were the quality of the leads?
Any good campaign obviously requires reporting and analysis. During and after your campaign has run, ask the sales people about the quality of the leads they were receiving. Were they aware of the brand and the products? Were they good prospects that had the budget and interest in buying? How long was the sales process? This feedback can help you to optimize and adapt your strategy to improve both your demand generation and lead nurturing processes and ultimately deliver better leads that will make their jobs easier and more profitable.

Having honest conversations with your sales team about your marketing initiatives and asking their expertise will show them that marketing campaigns are not just cost-centers, but revenue drivers that are working to help make their job, and the entire company, more successful.

For the Love of Marketing (and Sales): Why Alignment Matters

One of the sessions I attended at the SiriusDecisions Summit in Nashville last week posed the question, does alignment matter? It sounded like such a strange question because the very essence of our lives centers around alignment; alignment of body and mind, alignment of work and family, and of course, alignment of marketing and sales to company goals.

As our speaker explained, alignment (in the context of our discussion) was being defined as a common set of assumptions that marketing, product teams and sales organizations should align on in order to map activities and programs to the achievement of core company objectives. This common set of assumptions includes:
Identifying growth strategies, by markets, buyers, offerings, acquisition and productivity
Understanding where revenue is coming from, by segment, product and customer
Setting marketing coverage expectations, including brand, analyst relations, demand center, user conferences, creative services and analytics
Agreeing on marketing’s role in driving revenues, both sourced and influenced revenue
Defining and scoping marketing’s efforts, to include campaign portfolios, budget allocations, measurement cadence and reporting hierarchy
Developing and executing a functional plan encompassing campaigns, channels and marketing plans

So in other words, it’s really about alignment on what you’re doing (strategic alignment + goal setting), who’s doing it (marketing, product and sales) and how you’ll get there (marketing strategy + functional plan). The final, and most important aspect of alignment is actually taking the journey and adapting to change (execution + evolution).

The main focus of the alignment discussion though, was the reinforcement of a need for marketing, sales and product teams to join forces, conduct business reviews and assess the performance of activities together. Building a coalition of support across marketing, sales and product leaders is vital to bringing the teams together and aligning diverse activities to company objectives.

In the modern B2B buyer’s journey, where customers are progressing more than 60% of the way through a purchase before ever even engaging with a salesperson, organizations simply cannot afford to operate in silos. Marketing, sales and product teams must collaborate and leverage each other’s core competencies to achieve business goals.

What Does Sales Need to Do?
On the sales side, there needs to be a commitment to participating in the marketing planning process to ensure that sales initiatives are clearly understood. One of the more interesting recommendations I heard was to integrate a marketing person onto the sales planning team to drive this commitment.

What Does Product Need to Do?
Product teams should make sure that both marketing and sales organizations understand major product initiatives and how they can help. The guidance was to look beyond product launches and leverage a consistent cadence of programs to raise awareness and consideration for products.

What Does Marketing Need to Do?
On the marketing side, there must be a push for regular communication and increased collaboration with product and sales teams to reduce the likelihood of confusion, lost leads and missed opportunities. There must also be diligence on metrics focused on uncovering what’s performing well, what needs improvement, and what strategies should be considered moving forward.

Back to the original question of why alignment between marketing, sales and product teams matters, let’s think for a moment about misalignment. Misalignment isn’t a marketing problem, or a sales problem, or a product problem; it’s a business problem. And a business problem like this affects everyone in the organization, not just marketing, sales or product teams. Without alignment, a company simply cannot execute effectively.