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In this case study:
Client: Thermon Inc.
Industry: Manufacturing
Products and Services: Microsoft Dynamics 365, Dynamics Sales & Marketing
Country: USA
About Thermon
Thermon Inc. is a leading global provider of reliable and safe innovative thermal solutions since the 1950s. The organization specializes in providing complete flow assurance, process heating, temperature maintenance, freeze protection, and environmental monitoring. They offer modern solutions for industrial heating applications that serve the global energy, power generation, and chemical markets. The company operates in 25 countries with headquarters in Austin, Texas.
Quisitive previously worked with Thermon to implement an intranet solution to improve employee communication and collaboration. From project creation to completion, Thermon enjoyed the customer experience and built a strong relationship with the Quisitive team so when they needed help with their Dynamics 365 Sales environment, they didn’t hesitate to reach out. After the implementation of Dynamics 365 Sales, Thermon decided that they needed a partner, a Dynamics expert, to help them optimize and improve their customer engagement through sales and marketing channels. Quisitive was selected to help.
Challenge
Thermon’s Sales and Marketing teams identified priority areas that needed improvement to ensure data integrity, enhance the user experience, and deliver a consistent performance to ultimately enhance customer engagement. The company wanted to avoid inaccurate pictures of the customer life cycle and needed policies that could help them accomplish that.
Another goal was to integrate the CRM, ERP sales quotation function, and the email system to support the business’s requirements to develop an enterprise-wide view of the customer’s journey. Furthermore, the team needed to cut down on manual and time-consuming activities to save time and money, as well as reduce technical debt to take advantage of new Microsoft functionality as it is released.
Solution
Thermon needed help reimagining its Dynamics 365 Sales and Marketing environment to address their business priorities. Quisitive helped Thermon with a multi-phase approach that started with an innovation workshop to discover “the art of the possible” and identify near-term transformative activities. The purpose of this workshop was to collect feedback on what was working and learn more about their pain points. A prioritized roadmap for improvements was created after the workshop, which included the following components:
- Creating a single source of truth for customer data
- Achieving an enterprise-wide view of their customers
- Empowering sellers with 360-degree visibility into customer information in real-time
After the workshop, Thermon opted for Quisitive’s Dynamics Program, a subscription service that monitors and delivers strategic recommendations on a consistent basis. Thermon was paired with a dedicated Dynamics 365 Sales and Marketing coach and an entire team of experts to work with their team and ensure that their line-of-business solutions drive the most value for their organization. Key optimizations in the Dynamics 365 Sales and Marketing program included:
- Created archival/retention process for Dataverse data
- Rightsized Dynamics 365 Sales licenses by measuring usage and optimized spend
- Increased adoption of technology and encouraged use of existing features in the platform, such as the Dynamics search engine, lead routing engine as well as integrations with Power Automate
Furthermore, Quisitive supported the company’s organizational goals by partnering with their marketing team and leveraging platforms such as Dynamics 365 Sales, Scribe, and Power Automate to generate business opportunities and create a path from leads to closing a deal. This was accomplished by modernizing the solution, giving all stakeholders the needed visibility in the sales journey, and encouraging the use of the tools.
Results
Quisitive’s Dynamics Program offers Thermon strategic guidance to continuously optimize their Dynamics Sales and Marketing environment to improve customer engagement, employee experience, and drive continuous value for the organization. Quisitive enabled the organization to benefit from a real-time picture of the account status across the enterprise through improved system integration, better-automated workflows, an improved CRM user experience, and better overall system reliability.
Thermon’s Dynamics 365 Sales and Marketing capabilities will continue to adjust with the business’s needs and goals as they change, and Quisitive will continue helping them achieve their long-term goals. The Dynamics Program delivers a set of prescribed monthly activities and advisory meetings to keep companies’ systems environment healthy and secure. The next step in Thermon’s journey is to optimize the use of Dynamics 365 Marketing, a toolset intended to build meaningful relationships with customers and prospects with customer-led experiences.
Transformative Impact
- Improved licensing allocations and usage by right-sizing their Dynamics footprint
- Optimized the Sales and Marketing Dynamics environment to adjust as their business needs and goals change
- Increased completion of pipeline activity with system integrations between the ERP sales quotation, CRM, and email systems
- Improved user experience to maximize employee engagement to maintain accurate prospect/ customer data
- Stabilized system reliability to create monthly pipeline forecasting, a mission-critical business function
- Improved performance of the Dynamics 365 Sales and Marketing platforms by identifying areas where bottlenecks exist
- Created a more stable platform for their sales and marketing team
Marketing analytics measures the performance of marketing efforts. The goal of marketing analytics is to increase the effectiveness of advertising so that the advertiser gets the best possible return on their marketing investment.
The analysis of marketing techniques involves looking at data and finding information about customer activity, sales, engagement, and other factors. Machine learning provides a way to analyze large amounts of data.
This technology uses algorithms to find, categorize, and analyze data. It can automate sophisticated data collection and analysis tasks so that marketers have all the information they need to improve their campaigns right at their fingertips.
Furthermore, machine learning can provide predictive analytics that can use past and present data to foretell customer behavior and help foresee the effectiveness of planned marketing campaigns.
Understanding Machine Learning
Machine learning uses algorithms to perform data analytics and glean relevant information from vast amounts of data. It can also make predictions based on probability.
In marketing analysis, the predictive abilities of machine learning are quite useful; by predicting customer behavior, marketers can automate some tasks that would be time-consuming and expensive to perform manually.
For example, a company can deploy chatbots rather than actual customer service representatives to deal with customer questions and issues. These algorithms can also help predict customer behavior and target marketing campaigns, among other things.
Machine learning is a sub-field of the broader discipline of artificial intelligence (AI). Artificial intelligence covers all activities where machines carry out tasks or make decisions based on an automated set of parameters.
This involves giving computers or other machines access to large amounts of data and programming them to use this data to make decisions or perform analysis.
People often use the terms “machine learning” and “artificial intelligence” interchangeably. However, because marketing analytics involves processing and using massive amounts of data, people in this field use machine learning.
The Benefits of Machine Learning in Marketing
Machine learning can help improve customer experience, foresee customer needs, provide useful support, and help companies predict the effectiveness of marketing campaigns.
However, in some ways, machine learning has not lived up to expectations when it comes to marketing analytics. One of the main reasons for this is that companies do not have systems in place to handle the data that they receive. For example, they may not integrate the data from different sources, so they do not have enough information in one place to perform useful analytics.
Here are some ways that machine learning has already transformed marketing analytics.
Improving the Customer Experience
One of the most obvious ways that machine learning has improved marketing involves customer experience. For example, on e-commerce sites, machine learning enables personalized shopping recommendations based on real-time data.
A machine learning algorithm collects data about the customer’s browsing history. It finds patterns in this data and uses it to make personalized recommendations to each shopper. A good machine learning algorithm can produce recommendations based on real-time data, so the recommendations change each time a shopper visits a new page within the site.
Predicting Customer Needs
The predictive aspects of machine learning can forecast customer needs. By combining machine learning with big data, which requires large data sets from all across the internet, marketers can get enough information to make reasonably accurate predictions about future demand, industry trends, or customer needs.
Being able to predict customer needs can help businesses gain a competitive edge. For example, a company could create a product or a service that meets projected customer demands. Such early development could help them gain a competitive advantage in their industry.
Machine learning can also help define existing needs or areas of opportunity, such as gaps in service or product offerings. New products related to existing ones can help a company maximize revenue without having to develop entirely new products or services.
Optimizing Marketing Content
Advertising is not an exact science, but machine learning brings a scientific aspect to marketing campaigns by allowing analysts to see large amounts of data to measure marketing effectiveness.
Machine learning optimization can take a few different forms. First, marketers can use machine learning algorithms to compare advertisements or marketing strategies. These tests are sometimes known as A/B tests because they compare two different ads side by side. With enough data, marketing team members can easily see which choice performs better before investing in widespread ad placement.
Machine learning can also help provide insights about how users interact with an ad or other marketing content. This data lets marketers measure the success of a campaign and helps them make changes to marketing content to increase engagement.
Providing 24/7 Customer Service
Customer service can be an expensive operational cost for companies. Machine learning applications such as chatbots can help reduce these costs. Chatbots can answer basic questions and interact with customers on a fundamental level.
Chatbots can also collect data about customer concerns. This data can help companies fix a flaw in their product or service or improve chatbots by giving them more data to offer better customer service.
Chatbots can provide 24-hour service, and they can limit costs because they can handle customer issues without requiring an actual customer service agent. Companies can hire fewer customer service reps and lower their operational costs.
How to Enhance Your Marketing Efforts With Machine Learning
The process of adopting machine learning can vary depending on the needs and culture of a company. In some cases, you may need employees to buy into the idea of using artificial intelligence to aid marketing efforts. Management may need to take steps to convince experienced marketing personnel that the insights and tools available can improve marketing campaigns overall.
The next step is to integrate the machine learning applications into everyday operations. As with all technical solutions for businesses, this process involves teaching employees the technical aspects and, in the case of marketing, how to apply what it provides to existing practices and strategies.
IT employees, data scientists, programmers, or outside contractors will handle most of the technical aspects of machine learning for a marketing department. Most training, therefore, will involve making marketing personnel aware of the tools that they now have at their disposal when creating marketing campaigns, testing advertising, and measuring results.
Cloud-Based Data Collection & Analysis
Machine learning is a data-intensive undertaking. A cloud-based system provides the best infrastructure for combining machine learning with marketing analytics.
In the cloud, you can easily collect data from different sources and store it in one centralized place. All members of the marketing team can access the data from their offices or remotely on other devices. This easy access can increase productivity and make collaboration between people in different locations possible.
With a cloud-based system, you need a reliable cloud solutions provider to handle the platform and ensure that everything runs smoothly. The first step for a company that wants cloud-based systems is to move its current operations and workflow to the cloud. A third-party provider can make the cloud transition seamless.
A cloud services provider can also help you implement new machine learning tools and make changes that can help you get the most out of your system.
Make Changes Based on Actionable Insights
Machine learning uses data to provide insights that marketers can use to make changes or take specific steps. These insights can lead to operational improvements.
In addition to analysis, machine learning can help with operations. For example, an algorithm can help an e-commerce site or video streaming site offer personalized recommendations to each shopper or viewer based on their browsing and buying habits. This example of an operational application of machine learning has changed the customer experience on some of the world’s most popular websites.
Also, rather than replacing advertisements, machine learning can enhance campaigns by providing additional data. Data from a test of two marketing web pages, for example, can help marketers choose the most effective ads. They can also fine-tune elements within an ad by measuring customer interactions and behaviors.
With these tools, marketers can fine-tune a campaign while it is in progress. Also, they can gain enough data from current operations to inform future campaigns.
Most likely over the last 5 years you have invested tens of thousands if not hundreds of thousands of dollars in the latest emerging marketing technologies (martech) – email marketing, social media, A/B testing, account based marketing, CRM, digital asset management, etc. – starting to sound familiar?
Here we are five years later and you now have an ecosystem of disparate systems that are all managing content of various types through separate interfaces. While your intentions were noble, trying to enable your marketers to have the latest and greatest tools to do their job, you have inadvertently created a system that is expensive, difficult to manage and difficult to report against in a cohesive manner.
The Chief Marketing Officer (CMO) Council did their annual report on marketing technology called Context, Commerce + Customer, in which they asked senior marketing executives about marketing technology trends. One of the questions I considered to be the most valuable has do with gaps in technology in the current MarTech stack.
Thinking of the marketing and commerce technologies you have already implemented, are there any gaps you will look to fill in the coming year? The top 5 answers were:
These are some pretty large gaps to fill and can easily add more money and complexity to an existing marketing technology stack that is already overly complicated and expensive. Assuming that you fall into one of these categories, this may be the point where you want to start taking a more holistic view of your marketing ecosystem. Really start evaluating the total cost of ownership of these separate systems compared against the features and functionality that are necessary to be a fantastic digital marketer. As I wrote about in my previous post, “Outdated technology can mean winning at digital marketing,” the maturity of some of the core systems like Sitecore, may provide an opportunity to rationalize redundant applications and provide a more streamlined approach to developing and deploying digital content and campaigns. So how do you get started? Let’s take a 5 step approach to hacking your martech stack and find the areas where you can find greater efficiencies and cost savings.
- Start by defining the ideal processes. You are a seasoned digital leader, you know what it takes to be exceptional in the digital marketing game, so start by identifying your ideal state. Think through things like content management and workflow, campaign development, personalization strategies, account based marketing, translation workflows, social media, analytics – don’t forget to include marketing activities that are already working in addition to the ones you want to add. Attempt to sketch a diagram of how all these processes might work together, what types of data could get shared between these processes. Ignore, for the time being, your existing technology stack and internal processes to currently do these activities. Again, focus on the ideal state and not the current state.
- Map your current environment to the ideal state. This is a great place to see where there are gaps in your technology stack as well as within your own processes for managing the workload that additional activities will bring. An easy way to visualize this is to create a high level feature matrix, the columns being a list of activities that you mapped out in your ideal state, and the rows would be the systems that currently exist in your stack. This will serve to inform the areas that need to be focused on and or added. Another consideration in this phase is to evaluate how much of a particular tool is currently being used. For example, in marketing automation, systems like Marketo and Oracle Eloqua are fantastic enterprise marketing tools; but if you only really use 40% of the feature set that they provide, are you better off utilizing a system that provides reduced functionality but at a better price point?
- Evaluate other technologies vs current stack This is where the real potential exists in the rationalization game. Prior to the maturity of technologies like Sitecore Experience Platform, much of the functionality that marketers desired, existed in separate systems. Solutions such as Optimizely for A/B or multivariate testing, and Constant Contact for email marketing, and Google or Omniture for analytics. The maturation of Sitecore Experience Platform includes all of this functionality in a single platform and experience. Not only do you get the benefits of efficiency in managing all of your content in one place, but you get the added value of all of that data from testing, personalization and email activities wrapped into a single view of your company’s customers and prospects.
- Design the “perfect” stack At this point in the process you should now know where your gaps in your stack exist, what options exist to fill those gaps, and where you may have redundancies. This is the point where the rubber meets the road and you begin to make the difficult decisions about what technologies to keep, what to get rid of and what to bring on new. The truth is that making decisions like these only seem difficult. I’ll talk more in a future post about the road bumps to technology upgrades, but the single biggest hurdle to upgrades is that too much consideration is given to the time and money spent on the existing marketing stack. That sunk cost got you to where you are today, and that’s great, but marketing innovation requires a constant investment in technology. As you review your stack matrix, remember to think about areas where you can have a single technology or application that covers multiple pieces of functionality. Again, I point back to the Sitecore example above, having personalization, multivariate testing, email marketing and integrated analytics all in a single system could be a huge cost savings if you can rationalize out several other applications in the process.
- Create your roadmap Now that you have your ideal stack defined, it’s time to get to planning on how to implement. First, document your stack and prioritize the roadmap based on budget, resources and business need / value. I won’t go into detail here since every stack is different and implementation of technologies varies from system to system. What I will mention is that you are better off using an agile methodology for getting your roadmap implemented. Scott Brinker, of chiefmartec.com, wrote a fantastic book, Hacking Marketing, all about utilizing the principles of agile software development for the purposes of marketing. In short, work on small, bite-size chunks of functionality instead of trying to boil the entire ocean at once. For instance, if you know you’re going to do a new implementation, you can start with your infrastructure setup, then move to base system installation, then integrations, UX, content mapping, etc. Each of these can be an iterative phase of the project instead of trying to do the entire thing in one fell swoop.
Finally, get started today. Your technology stack is only getting older and you are only falling more behind the longer you wait to start. If you think you don’t have time, then step back and ask yourself, “How much time and money would be saved if my marketing stack were up-to-date and efficient?”
Once you’ve decided that you need a marketing automation solution you have two choices: you can jump right in and starting setting it up and try to figure it out, or you can take a little time and plan the implementation. Most people will agree that taking some time is the right choice even though it is not always the way it happens in reality. So when you’re ready to buy a marketing automation solution, consider these five things before you purchase and deploy.
Start by understanding your current process for marketing to your customers as well as the pieces you would like to add once you have a solution in place. Marketing Automation will help automate a lot of steps, but if there are problems in your marketing process, then the solution will only amplify them by automating bad processes. Have an understanding of the customer journey, which includes the steps a customer goes through while interacting with your company on a purchase path. Understanding the potential touch points allows you to define what the customer expects at each step and stage. Does the customer need an article that helps them understand the size of the problem, or do they need help justifying the costs, or maybe they need to talk to a salesperson to get a demo of your offering? Understanding the steps allows you to determine the content or actions needed at each point. Then you’ll be able to build a full process that will help a prospect move down the purchase path with the right information at the right time so that they can make a decision.
Deploying Marketing Automation cannot happen in a silo, as the results will not be ideal and the projects will often fail. Deploying a solution like this will require the cooperation of a variety of departments, most often including Marketing, Sales, and IT, but it can include other departments like BI or Decision Support, Operations, and others. To be an effective solution, marketing automation solutions often need to integrate with other solutions that are owned by IT or other departments. Make sure that data sources such as lead scoring models are based on data and insights from sales results. This will allow your automation process to be as close to reality as possible.
Marketing Automation can be complicated. There are a lot of moving parts to organize and manage when developing campaigns, but then you must apply all of those pieces to the technical solution that you purchased so that you can execute the campaign as you imagined. Hiring a partner with experience in marketing automation, or hiring a dedicated employee to own the marketing automation solution can greatly improve the results of any marketing automation program. Marketing departments are continually asked to do more with less, but this is an important step that you don’t want to skip. Having someone who understands the technical capabilities of your solution and also understands the art/science of marketing will prove invaluable as you begin to implement campaigns and get the most out of your system. An effective partner can help you short cut the process from starting to effectively running campaigns because they have experience and an understanding of the tools, theory, and execution.
Start with a simple campaign and build from it. You don’t have to build every campaign that your company currently does into the solution to get started. Pick something that easily lends itself to marketing automation and start there. A great starting point is automating a campaign around a trade show or event. There are two benefits to starting with a campaign like this. One, the campaign is usually short in duration so that you can set it up and run it and see results in a short amount of time. Secondly, the content needed is usually pretty well defined. You will likely have a message before the event inviting registrants to visit your booth or attend your session. Then you might have a message during the event encouraging people to stop by and enter to win your giveaway or inviting them to take some other action. Then, you can have a message that recaps the event and have it scheduled to send three days after the event ends. Additionally, you can load the list of attendees who visited your booth so that they get a personalized email thanking them for stopping by and encouraging them to take the next steps with your company.
These are straightforward content pieces that can be easily created, and set up easily into an automated campaign. This will give you results quickly and allow you to get your feet wet without much risk.
The final step, and perhaps the most important, is designed to focus and motivate your team throughout the process of implementing marketing automation. I’ll be blunt, it’s not always the easiest process. Implementing the technology might be straightforward, but changing the behaviors of users who are learning the system, developing campaigns, creating content, and most importantly, understanding how to get the most value out of your system will take time and energy. When starting the process as a team, create a vision statement and a why definition. The vision is what you want to accomplish with the project and includes the metrics that you want to affect. You don’t have to define the actual numbers, but ideally you should know how you plan to measure success. The why is the reason you are doing this project, the motivation, and the outcome that you hope to see as a result. This can be aspirational, but make sure to be realistic. Having agreement from your team will help you focus and make progress as you move through the entire process.
By taking some time to plan your marketing automation implementation you can help ensure that your project will be successful by being better prepared to leverage the power that a marketing automation solution provides to you and your team.