Data science for sales

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Are you wondering what kinds of problems data science teams can help sales teams solve? Or maybe you are more interested in hearing about specific examples of data science projects that can have an impact in the sales domain? Well either way, you are in the right place!

In this article, we tell you everything you need to know about how data science can be used to help sales teams. We start out by discussing the problems that data science teams can help sales teams solve and the business metrics data science teams can help sales teams move. After that, we provide specific examples of data science projects for sales. Finally, we discuss what types of datasets are most important for data science teams that support sales teams to have access to.

How can data science teams help sales teams?

Problems data science teams can help sales teams solve

What are some common problems that sales teams have that data science teams can help solve? Here are some problems that data science teams can help sales teams solve.

  • Prioritizing the right leads. The first way that data science teams can help sales teams is by making sure that the sales team is prioritizing the right leads. No matter whether the sales team is working through a queue of inbound leads that have already expressed interest in a product or a list of outbound prospects that have not expressed interest, the data science team can help the sales team make sure that they are spending their time engaging with the right leads.
  • Understanding leads and how to interact with them. Once the team has narrowed in on the pool of leads that the sales team should be engaging with, the next step is to help the sales team understand those leads and their unique situations. This will help the sales team interact more effectively with leads and present leads with information that is most compelling to them.
  • Streamlining planning and operations. Finally, the data science team can also help the sales team with streamlining their planning and operations. This will ensure that the team is properly staffed, trained, and set up to succeed for the upcoming period.

Metrics data science teams can help sales teams improve

What kinds of metrics can data science teams help sales teams to improve? Here are some metrics that data science teams can help sales teams improve.

  • Deal close rate. The first metric that data science teams can help sales teams improve is their deal close rate. Data science teams can help sales teams improve this metric by ensuring that they are prioritizing the right leads and approaching them with the right messaging.
  • Attributed revenue. Data science can also help sales teams with increasing the amount of revenue that is attributed to their efforts by helping the sales team identify leads that are both likely to convert and likely to bring a large amount of revenue. Streamlined planning and operations can also help to ensure that the team has the resources and staffing necessary to address demand.

What are some examples of data science projects for sales?

What are some examples of data science projects that can help sales teams achieve their goals? In this section, we will provide specific examples of data science projects for sales. We will specifically focus on projects that require machine learning or advanced modeling to complete. We will organize these projects into groups based on the problem that each project helps the sales team solve.

Problem 1: Prioritizing the right leads

First, we will talk about ways that the data science team can help to ensure that the sales team is prioritizing the right leads and talking to the right people. Here are some ways that data science teams can help to ensure that the sales team is spending time on the leads that are the most likely to convert and bring the most value to the business.

Project 1: Understanding the value a customer will bring to the business

The first way that the data science team can help the sales team meet their goals is by providing the sales team with better visibility on how much revenue a given lead is likely to generate. This will help to ensure that the sales team spends more time talking to high value leads that will bring in more revenue. This is particularly important for sales teams that have targets or quotas that are tied back to revenue.

A customer lifetime value model is a common type of model that is used in these types of situations. Customer lifetime value models are models that aim to predict how much value a given lead will contribute to the company. They can be built using data coming from a variety of sources such as interactions with the sales team, actions taken on the company website, attributed traffic sources, information provided in applications, and external information provided by third party vendors.

If you do not have external sources of data that can be used to enrich the knowledge you have about leads, then you may only be able to use this strategy to prioritize inbound leads that have expressed interest in your product. However, if you have supplemental information about prospects that have not yet expressed interest in your product, then you can also create this type of model for prospects for outbound sales targeting as well.

Project 2: Understand which leads are most likely to convert

The next way that data science teams can help sales teams operate more effectively is by helping them understand which leads are more likely to convert and actually become paying customers. If the sales team has a better idea of which leads are most likely to convert, they can focus more time on promising leads that are likely to join and less time on leads that are very unlikely to convert.

These models that are used to predict how likely a lead is to convert are often called lead scoring models. These are typically machine learning models that can be used to determine the probability of conversion. They typically use similar kinds of data as customer lifetime value models that are built for leads in the sales pipeline. This includes things like interactions with the sales team and website as well as information about the lead that was provided by the lead or obtained through an external source.

These types of models are easier to create for inbound leads where customers have already provided information about their intent. Much like customer lifetime value models, lead scoring models can only be used to prioritize outbound leads if your company has an external datasource that can provide sufficient information about prospects for outbound sales interactions. If you have enriched data sources on outbound prospects, then lead scoring models can also be built for outbound prospects.

Project 3: Understand which leads are most likely to convert if and only if they are engaged with

While knowing which leads are most likely to convert and become paying customers is better than nothing, the real piece of information that would be most useful to know is which leads are most likely to convert if (and only if) they are engaged with. This could help you distinguish between leads that are likely to convert whether the sales team goes out of the way to engage with them or not from leads that are likely to convert only if the sales team engages with them. Having this kind of information allows the sales team to focus their efforts where they are needed most – on the segment of customers that is specifically likely to convert if they are engaged with.

An uplift model is a fairly common type of data science model that is used to determine which subjects are most likely to respond well to a particular treatment or intervention. This is a common class of models that is often applied to solve problems like these and help the sales team identify which leads they are likely to get the most outsized benefit from talking to. These models are generally built using the same type of data that is used to build lead scoring models.

Much like standard lead scoring models, the ability to use these models for outbound sales prospecting depends on whether you have an external data source that can be used in order to obtain information about the prospects on your outbound prospect list. These types of models are generally applicable for inbound leads regardless of whether you have an external data source or not.

Problem 2: Understanding leads and how to interact with them

The next problem that data science teams can help sales teams with is understanding more about the leads in their pipelines. This information can help the sales team better understand how to interact with these leads.

Project 1: Understand who your customers and leads are

In order to understand what kind of messaging and interactions will be most persuasive for your customers, you must first understand who your customers are and what the different customer personas you serve are. If you understand who your customers are then you can better understand their needs and paint points so that you can personalize the messaging you use depending on what customer persona you are dealing with.

Unsupervised learning models such as k-means clustering are commonly used to build these kinds of models that are used to help understand customer segments. Unlike many of the other models we have discussed in this article, these models do not have a specific outcome that is predicted. Instead, they serve to group similar subjects together into clusters of observations that have similar characteristics. Once you have these clusters, you can do more exploratory analysis to understand the unique attributes and needs associated with each cluster.

Depending on exactly what you are looking to understand, you can either build these segmentation models using data from existing customers that are already using your product or using data from leads who have not yet onboarded onto your platform. The exact type of data that is used to build these types of models depends on what data you have access to.

Project 2: Identify leads with specific attributes to help tailor your sales pitches

Sometimes knowing a simple piece of information about a lead can make it much easier to tailor a sales pitch to them. For example, if you are selling vacuum cleaners then it might be useful to know which leads have pets so you can tailor a sales pitch around how your vacuum is particularly well suited to cleaning up pet hair. The problem is – how do you determine which leads have attributes that are relevant to you? This is another problem that data science teams can help solve.

Data science teams can build supervised learning models that aim to predict specific attributes about customers so that sales teams can better tailor their sales pitches. These types of models can be built using standard supervised learning models like regression models and gradient boosted tree models. These types of models are typically built using data that is available early in the customer acquisition funnel such as data on sales interactions, website interactions, or other attributes that were provided by the lead themselves or an external data source.

Project 3: Cross-sell and upsell models to understand which products to offer to which customers

The next type of models we will talk about are cross sell and upsell models. These are models that are used to target customers that are already using your services (or are about to sign up for your services) to try to get them to use additional services and add ons. The idea here is that the customer will produce more revenue for your company if they use more of your services.

These types of models are different from other models that we have discussed in the past. Most of the other models we have discussed in this article are primarily useful for targeting new customers that are not using your product. These models, on the other hand, are often used to identify opportunities to increase the footprint of your relationship with an existing customer. That means that you have a lot more data to work with because you have a wealth of information related to the actions that the customers have taken while they have been using your product.

This is another class of problem that can often be solved using traditional supervised learning algorithms like regression models and gradient boosted tree models. Recommender systems can also be used for these purposes.

Problem 3: Streamlining planning and operations

Data science teams can also build models that help sales teams streamline their planning and operations. These models can help to set the team as a whole up for success by ensuring that the team is properly staffed and trained. Here are some examples of data science projects that can help the sales team with planning.

Project 1: Understand the amount of sales expected to close in a given period of time

One way that data science teams can help sales teams to plan their operations is to create forecasts that estimate how many sales are expected to close in a given time period. When sales managers and operations specialists have an understanding of the number of sales that are expected to close in a given time period, they can ensure that the team is properly staffed to meet the demand. They can also better set goals and track progress against forecasts to understand how the team is delivering.

As an added bonus, these types of models can also be used by downstream operations teams to better estimate their staffing needs. For example, if the sales forecast says that the size of the customer base is expected to double over a six month period then other teams like support teams might need to increase their headcount to ensure they have enough trained workers in place by the time this happens.

Sales forecast models like this are typically created using previous sales data as well as macroeconomic data and any external data that might be useful. These forecasts are usually implemented using statistical time series models.

Project 2: Forecasting where demand for the product will be concentrated

In addition to forecasting how many deals the sales team will sign, data science teams can also build forecasts to help understand how demand for the product will be distributed. For example, if the sales team is partitioned based on physical locations that they cover then the data science team can build forecasts to help understand how demand for the product will be spread across different territories. If the sales team is responsible for selling multiple different products or service levels, the data science team can build forecasts to help understand how demand will be spread across different services.

When sales managers and operations specialists have a better idea of how demand will be spread across different segments, they can ensure that the team is staffed and cross-trained appropriately. For example, if the demand for one particular service is forecasted to rise rapidly then they can ensure that many people are cross-trained and able to sell that product.

These types of demand forecasting models are generally built out in a similar way to aggregate sales forecasts. They are implemented using statistical time series models and previous demand or sales trajectory data.

What kind of data is important for teams supporting sales?

What kind of data is used by data science teams that support sales teams? Here are some of the most common types of data that are used in data science for sales teams.

  • Website interactions. The first type of information that is useful for data science teams that support sales teams is website interactions. This encompasses all kinds of interactions that lead might have with the company website as they explore their offerings and express their interest in the product. This type of information is typically only available for inbound leads that have come to the company’s website and submitted their contact information.
  • Lead source. It is also useful for data science teams who are supporting sales teams to have information on the sources that their leads are coming from. Different traffic acquisition sources tend to bring in different types of people from different demographics. This means that leads that come from different sources are inclined to have different behaviors and tendencies.
  • Sales interactions. The next type of data that is useful for data science teams that support sales teams is sales interactions. This encompasses any kind of trackable interactions that the lead has with the sales team. Oftentimes information that is garnered during these interactions is captured in a CRM tool like Salesforce or Hubspot.
  • Attributes provided by leads. It is common for websites to have application forms that customers must submit in order to express their interest in the product and contact the sales team. The information provided in this form can be very valuable for data teams. It is especially valuable for models that score customers before they even come into contact with the sales team, such as models that help sales teams prioritize inbound leads.
  • Attributes provided by external sources. Another important source of customer data is external data that is provided by third party vendors or other parties that your company has data sharing agreements with. There are many vendors that exist with the primary purpose of providing businesses with enriched data that they can use for things like sales and marketing operations. This type of information is particularly useful for prioritizing prospects for outbound leads that have not interacted with your website or product in any other way.
  • Signups. Finally, information on which customers signed up for the product is required to evaluate which interventions and tactics actually produced the desired result.

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