Article | February 18, 2021
Your sales cycle encompasses every action you take to close a new customer as a salesperson. But there is a possibility for the sales cycle to be confused with sales methodology. Sales methodology is a framework in which one practices a sales cycle. Whereas the sales cycle is the step-by-step process of you, as a salesperson, to close a deal with a client.
Piper Drive, a sales CRM and pipeline management platform defines a sales cycle as the series of predictable phases required to sell a product or a service, and that sales cycles can vary greatly among organizations, products, and services, and no one sale will be the same.
Especially with the healthcare industry, a thorough understanding of your health tech sales cycle will make your sales operations more efficient. Shortening the sales cycle without an up-front investment for sales is one of the critical healthcare sales and marketing goals. If you shorten your health tech sales cycle, you get more time to make additional leads. This will ultimately result in having an improved bottom line.
A faster and shorter sales cycle can bring you more advantages in the competitive sales world of your industry. It will indeed allow your company to grow its business by improving market share. Have you ever thought, as a salesperson, about the effective ways to shorten the health tech sales cycle? This article mainly focuses on proactive ways to shorten your sales cycle and improve profitability.
Challenges of Long B2B Health Tech Sales Cycle
According to Marketing Sherpa, a market research institute, the length of the sales cycle can vary from industry to industry. Comparatively, the health tech industry has a longer sales cycle. Still, there are many effective ways to shorten it and bring a positive impact on your sales process.
In general, B2B sales take a lot of time to maintain. Thus, the B2B health tech sales cycle takes even months to close a sale with a prospect and faces many challenges in the process. Some of the challenges you may face, as a health tech salesperson, can be the following on the process:
Turning the Lead to a Sales-Ready Prospect
No health tech salesperson will find a lead ready to make the sale without any persuasion from you. In the health tech sales cycle, lead nurturing should be your best bet to convert a prospect.
With longer sales cycles, it won't be easy to nurture a lead all through the process and make a sales-ready prospect. It would be easier to convert leads when they are ready if you keep in touch with the prospects. People often find it difficult to do so in the long health tech sales cycle and end up not converting potential clients into happy customers.
Maintaining Engagement Over Time
The sales team keeps converting leads on their radar. As days and months pass, it is challenging to memorize each prospect you have interacted with. Neglecting them brings nothing to your business.
It can be a juggle to balance new prospects with existing SQLs. Older ones may get lost in the weeds as new leads come in. No one can tell which one is a higher priority. Whom will you pay more attention to and for how long? It can be a severe obstacle in the long B2B health tech sales cycle.
Keeping Your Sales Team Energized
If your sales team is not engaged with the process itself and enjoys it, they will have a more challenging time dealing with leads. It is a fact that, unfortunately, salespeople can become frustrated or bored due to working with difficult and hesitant leads.
As the health tech sales cycle drags on, it is tough to remain emotionally calm. If you have no strategies to energize them promptly, apathy or discouragement may come into play.
Ensuring Marketing and Sales Alignment
Lack of communication between the sales and healthcare marketing teams can pose the most detrimental challenges. It can impact the health tech sales cycle seriously. This loss of alignment between marketing and sales can hurt lead nurturing and lead to further difficulties like the ones listed above.
When the two teams move out of sync, it often requires a lot of effort to get them on the same page again. These teams can work separately with decisions and different goals, but it would not benefit the entire company.
Five Stages of the Typical Sales Cycle
It is better to understand the five stages of the health tech sales cycle to comprehend how the sales cycle comes into play completely.
This is the stage where the sales team attracts leads and listens to them, and learns to offer what they need.
This is the stage for you to get leads to move forward with your offers. You can utilize all the data you have amassed during prospecting.
As a salesperson, this is when you learn about leads and determine whether the prospect intends to buy.
In this stage of the health tech sales cycle, you offer your product or services as an effective solution for your lead's pain points.
It’s a fit! By now, you know if your lead wants your product or service and move forward.
Benefits of Shortening your Sales Cycle
A shorter health tech sales cycle allows you to meet more prospects within the same time frame. For example, if you take one week for each prospect to complete the cycle, you can meet more people than with a two-week average life cycle for a single prospect. As you meet more people, it allows you to make more money. Moreover, most of the prospects prefer to have shorter sales cycles provided that you fulfill their need and solve their problem.
However, even with a short health tech sales cycle, you should have an effective method to track sales information. Along with a short sales cycle, an effective method will increase your team’s efficiency and sales numbers. You will make more profits and improve your sales cycle.
Ways to Shorten your Long Health Tech Sales Cycle
One of the significant challenges faced by healthcare technology salespeople is shortening their health tech sales cycle. Unlike B2C, the B2B process of sales has to deal with many decision-makers and educate them about the value of your products. Typically, it takes a lot of time and effort to convince your prospects that your solution is customized to meet their unique needs.
However, your health tech sales cycle can become agonizingly long as each prospect can have a different perspective about your solution. It can also occur due to the number of people involved in the decision-making in B2B companies. According to the latest Demand Gen Report, the buyer’s journey is getting more complicated and longer. This makes the sales process worse, which is already tedious.
However, the good news is that you can follow these marketing strategies to shorten your health tech sales cycle tactically.
Understand Your Buyer Personas
Keep yourself away from trying to engage the wrong people. This will not bring you any results in the end. Before commencing the sales process, you should have a clear idea of who your targeted audience is. The decision-makers or influencers in the organizations you are targeting are your buyer personas.
After identifying your personas, by answering the following questions, you can outline their qualities:
• What are their goals?
• What are their responsibilities?
• What trigger drives them to buy?
• What problems are they dealing with?
• How do they like to research?
• What inhibits a purchase?
As you answer these questions, you will get a clear idea of the best way to approach them.
Send an Introductory Video
The prospects get to know the salesperson only in the in-person meeting. So before the in-person meeting, you can consider sending them an introduction video. This would add value and explain why you are interested in them with a ‘call-to-action. It creates familiarity by the time you connect with them. This is a very creative step to shorten your health tech sales cycle.
Provide Pre-Sales Appointment Content
Having a sales appointment with a prospect, who does not know anything about your solution, is one of the biggest mistakes you can make in your health tech sales cycle. This means you may have to have several meetings to convince the client. This problem can be eliminated with a lead nurturing email with informative content. This email can have a link to an informative blog about your product, which was written previously. It will make them peruse your website before the actual sales meeting with you. It saves your time by eliminating many meetings to educate the prospect about your product.
Provide Post-Sales Appointment Content
The prospect is expected to come out with some concerns and objections after the first sales appointment. As a healthcare tech company salesperson, it is your responsibility to eliminate all those obstacles by addressing them strategically. Sending follow-up emails, videos, and case studies helps address those concerns. The content can be used to guide other prospects too. Overcoming these obstacles with effective content can shorten your health tech sales cycle effectively.
Come Clean with Pricing
Pricing of your products can be one of the main concerns for your prospects. Many salespeople address it only at the last stage of the health tech sales cycle. Not revealing the price at the beginning will only lengthen your process. Moreover, it may result in losing trust in you. Be transparent and reveal the price, to save yourself from such issues.
Leverage Social Proof
One of the smartest ways to win the trust of your prospect is to provide social proof. It will also make the deal close sooner. The best social proofs are case studies with the impact of your products or ROI. Remember to make that the company featured in the case study is similar to the particular prospect's company.
In the health tech sales cycle, it is crucial to thoroughly understand the prospect. Your sales cycle should connect with the process of lead nurturing, where you act as an advisor. This will also help you build trust with the prospects and increase your chances of closing the deal before the expected time.
Executing all of these alone would be challenging. At Media7, we help companies market their healthcare technology product with innovative strategies and support by implementing these strategies effectively. Our strategies help attract prospects, convert them and turn them into your happy customers forever. To know more about us, visit https://media7.com/
Frequently Asked Questions
What are the stages in the health tech sales cycle?
B2B health tech sales cycles include seven main stages. They are sales prospecting, making contact, qualifying the lead, nurturing the lead, making an offer, handling objections, and closing the sale. Following these steps help a salesperson to close the sale effectively.
How does the health tech sales cycle help?
The health tech sales cycle helps you identify potential clients and nurture them through the process of sales. It makes you effectively and efficiently guide your clients and gives them the confidence to go forward with more effort.
What are the best practices for the health tech sales cycle?
The best practices for the health tech sales cycle can be attracting more prospects through content marketing, building trust by understanding clients better, focusing on your customers' clients, and knowing the prospect’s organizational chart.
Why is the sales cycle important in health tech?
The sales cycle in the health tech industry helps the sales managers to forecast the accurate picture of your sales. This because they know where your salespersons are in the sales cycle. It also gives a clear picture of how many deals your salespeople close out of a given number of leads.
Article | March 10, 2020
The decade’s first global health crisis has placed the spotlight on the need for healthcare technology that can prevent and solve the world’s critical health challenges. This last week, Microsoft shared progress on innovations helping to meet these objectives. Today, we’re spotlighting intelligent health and business solutions from Microsoft Business Applications that empower health providers to help transform operations and deliver better patient experiences, better insights, and better care. Healthcare organizations are leveraging Dynamics 365 and Microsoft Power Platform to improve both provider operations and patient outcomes. These customers are prime examples of our focus to enable tech intensity across healthcare, empowering organizations to develop their own digital capabilities that use data and AI to address challenges and tackle new opportunities. Across healthcare, quality of care is increasingly dependent on synchronizing operations across staff to gain greater efficiencies and accelerate decision-making.
Article | December 8, 2020
A cruelly ironic truth is that nurses and other caregivers assisting injured and ill patients often wind up injured themselves. In fact, the caregiver profession has among the highest rates of injury, with back injuries being the most common and the most debilitating. Every year, more than 10% of caregivers leave the field because of back injuries. More than half of all caregivers will experience chronic back pain.
Most back injuries to caregivers happen when lifting patients from beds or wheelchairs. Injuries can occur instantly, but they can develop over time as well, often without the caregiver’s awareness. For example, the caregiver can sustain disc damage gradually and not feel any pain, and by the time he or she does experience pain, there can already be serious damage.
Article | September 4, 2020
A digital twin is a digital representation of a real-world entity or system. The implementation of a digital twin is a model that mirrors a unique physical object, process, organization, person or other abstraction. For healthcare providers, digital twins provide an abstraction of the healthcare ecosystem’s component characteristics and behaviors. These are used in combination with other real-time health system (RTHS) capabilities to provide real-time monitoring, process simulation for efficiency improvements, population health and long-term, cross-functional statistical analyses.
Digital twins have the potential to transform and accelerate decision making, reduce clinical risk, improve operational efficiencies and lower cost of care, resulting in better competitive advantage for HDOs. However, digital twins will only be as valuable as the quality of the data utilized to create them. The digital twin of a real-world entity is a method to create relevance for descriptive data about its modeled entity. How that digital twin is built and used can lead to better-informed care pathways and organizational decisions, but it can also lead clinicians and executives down a path of frustration if they get the source data wrong. The underlying systems that gather and process data are key to the success for digital twin creation. Get those systems right and digital twins can accelerate care delivery and operational efficiencies.
Twins in Healthcare Delivery
The fact is that HDOs have been using digital twins for years. Although rudimentary in function, digital representations of patients, workflow processes and hospital operations have already been applied by caregivers and administrators across the HDO. For example, a physician uses a digital medical record to develop a treatment plan for a patient. The information in the medical record (a rudimentary digital twin) along with the physician’s experience, training and education combine to provide a diagnostic or treatment plan. Any gaps in information must be compensated through additional data gathering, trial-and-error treatments, intuitive leaps informed through experience or simply guessing. The CIO’s task now is to remove as many of those gaps as possible using available technology to give the physician the greatest opportunity to return their patients to wellness in the most efficient possible manner.
Today, one way to close those gaps is to create the technology-based mechanisms to collect accurate data for the various decision contexts within the HDO. These contexts are numerous and include decisioning perspectives for every functional unit within the enterprise. The more accurate the data collected on a specific topic, the higher the value of the downstream digital twin to each decision maker (see Figure 1).
Figure 1: Digital Twins Are Only as Good as Their Data Source
HDO CIOs and other leaders that base decisions on poor-quality digital twins increase organizational risk and potential patient care risk. Alternatively, high-quality digital twins will accelerate digital business and patient care effectiveness by providing decision makers the best information in the correct context, in the right moment and at the right place — hallmarks of the RTHS.
Benefits and Uses
Digital Twin Types in Healthcare Delivery
Current practices for digital twins take two basic forms: discrete digital twins and composite digital twins. Discrete digital twins are the type that most people think about when approaching the topic. These digital twins are one-dimensional, created from a single set or source of data. An MRI study of a lung, for example, is used to create a digital representation of a patient that can be used by trained analytics processes to detect the subtle image variations that indicate a cancerous tumor. The model of the patient’s lung is a discrete digital twin. There are numerous other examples of discrete digital twins across healthcare delivery, each example tied to data collection technologies for specific clinical diagnostic purposes. Some of these data sources include vitals monitors, imaging technologies for specific conditions, sensors for electroencephalography (EEG) and electrocardiogram (ECG). All these technologies deliver discrete data describing one (or very few) aspects of a patient’s condition.
Situational awareness is at the heart of HDO digital twins. They are the culmination of information gathered from IoT and other sources to create an informed, accurate digital model of the real-world healthcare organization. Situational awareness is the engine behind various “hospital of the future,” “digital hospital” and “smart patient room” initiatives. It is at the core of the RTHS.
Digital twins, when applied through the RTHS, positively impact these organizational areas (with associated technology examples — the technologies all use one or more types of digital twins to fulfill their capability):
Clinical communication and collaboration
Next-generation nurse call
Alarms and notifications
Integrated patient room
Digital Twin Usability
Digital twin risk is tied directly to usability. Digital twin usability is another way of looking at the issue created by poor data quality or low data point counts used to create the twins. Decision making is a process that is reliant on inputs from relevant information sources combined with education, experience, risk assessment, defined requirements, criteria and opportunities to reach a plausible conclusion. There is a boundary or threshold that must be reached for each of these inputs before a person or system can derive a decision. When digital twins are used for one or many of these sources, the ability to cross these decision thresholds to create reasonable and actionable conclusions is tied to the accuracy of the twins (see Figure 2).
Figure 2: Digital Twin Usability Thresholds
For example, the amount of information about a patient room required to decide if the space is too hot or cold is low (due to a single temperature reading from a wall-mounted thermostat). In addition, the accuracy or quality of that data can be low (that is, a few degrees off) and still be effective for deciding to raise or lower the room temperature. To decide if the chiller on the roof of that patient wing needs to be replaced, the decision maker needs much more information. That data may represent all thermostat readings in the wing over a long period of time with some level of verification on temperature accuracy. The data may also include energy load information over the same period consumed by the associated chiller.
If viewed in terms of a digital twin, the complexity level and accuracy level of the source data must pass an accuracy threshold that allows users to form accurate decisions. There are multiple thresholds for each digital twin — based on twin quality — whether that twin is a patient, a revenue cycle workflow or hospital wing. These thresholds create a limit of decision impact; the lower the twin quality the less important the available decision for the real-world entity the twin represents.
Trusting Digital Twins for HDOs
The concept of a limit of detail required to make certain decisions raises certain questions. First, “how does a decision maker know they have enough detail in their digital twin to take action based on what the model is describing about its real-world counterpart?” The answer lies in measurement and monitoring of specific aspects of a digital twin, whether it be a discrete twin, composite twin or organization twin.
Users must understand the inputs required for decisions and where twins will provide one or more of the components of that input. They need to examine the required decision criteria in order to reach the appropriate level of expected outcome from the decision itself. These feed into the measurements that users will have to monitor for each twin. These criteria will be unique to each twin. Composite twins will have unique measurements that may be independent from the underlying discrete twin measurement.
The monitoring of these key twin characteristics must be as current as the target twin’s data flow or update process. Digital twins that are updated once can have a single measurement to gauge its appropriateness for decisioning. A twin that is updated every second based on event stream data must be measured continuously.
This trap is the same for all digital twins regardless of context. The difference is in the potential impact. A facilities decision that leads to cooler-than-desired temperatures in the hallways pales in comparison to a faulty clinical diagnosis that leads to unnecessary testing or negative patient outcomes.
All it takes is a single instance of a digital twin used beyond its means with negative results for trust to disappear — erasing the significant investments in time and effort it took to create the twin. That is why it is imperative that twins be considered a technology product that requires constant process improvement. From the IoT edge where data is collected to the data ingestion and analytics processes that consume and mold the data to the digital twin creation routines, all must be under continuous pressure for improvement.
Include a Concise Digital Twin Vision Within the HDO Digital Transformation Strategy
Digital twins are one of the foundational constructs supporting digital transformation efforts by HDO CIOs. They are digital representations of the real-world entities targeted by organizations that benefit from the advances and efficiencies technologies bring to healthcare delivery. Those technology advances and efficiencies will only be delivered successfully if the underlying data and associated digital twins have the appropriate level of precision to sustain the transformation initiatives.
To ensure this attention to digital twin worthiness, it is imperative that HDO CIOs include a digital twin vision as part of their organization’s digital transformation strategy. Binding the two within the strategy will reinforce the important role digital twins play in achieving the desired outcomes with all participating stakeholders.
Building new capabilities — APIs, artificial intelligence (AI) and other new technologies enable the connections and automation that the platform provides.
Leveraging existing systems — Legacy systems that an HDO already owns can be adapted and connected to form part of its digital platform.
Applying the platform to the industry — Digital platforms must support specific use cases, and those use cases will reflect the needs of patients, employees and other consumers.
Create a Digital Twin Pilot Program
Like other advanced technology ideas, a digital twin program is best started as a simple project that can act as a starting point for maturity over time. Begin this by selecting a simple model of a patient, a department or other entity tied to a specific desired business or clinical outcome. The goal is to understand the challenges your organization will face when implementing digital twins.
The target for the digital twin should be discrete and easily managed. For example, a digital twin of a blood bank storage facility is a contained entity with a limited number of measurement points, such as temperature, humidity and door activity. The digital twin could be used to simulate the impact of door open time on temperature and humidity within the storage facility. The idea is to pick a project that allows your team to concentrate on data collection and twin creation processes rather than get tied up in specific details of the modeled object.
Begin by analyzing the underlying source data required to compose the digital twin, with the understanding that the usability of the twins is directly correlated to its data’s quality. Understand the full data pathway from the IoT devices through to where that data is stored. Think through the data collection type needed for the twin, is discrete data or real-time data required? How much data is needed to form the twin accurately? How accurate is the data generated by the IoT devices?
Create a simulation environment to exercise the digital twin through its paces against known operational variables. The twin’s value is tied to how the underlying data represents the response of the modeled entity against external input. Keep this simple to start with — concentrate on the IT mechanisms that create and execute the twin and the simulation environment.
Monitor and measure the performance of the digital twin. Use the virtuous cycle to create a constant improvement process for the sample twin. Experience gained through this simple project will create many lessons learned and best practices to follow for complex digital twins that will follow.