How does PandaDoc work effectively with lead scoring?
Victor Kuvshinov, Head of Product PandaDoc told at the Epic Growth Conference product marketing conference how and what is used for lead learning in Sales-assisted or in mixed Self-service / Sales-assisted products using the example of PandaDoc.
Watch the video and read the decoding under the cut.
In general, I will tell you how to enable the sales department to be more efficient and to multiply the growth of your product.
')
Lead scoring is the methodology for evaluating and ranking each individual lead according to the interest it represents for your business. In simple terms, the method of segmentation of potential customers who are in the sales funnel.
Under this methodology, you score points for every perfect lead action and for every quality characteristic he possesses. For example, where did the lead come from, what company does it represent, what position does it take, what actions has it done inside your application. The more points credited to the lead, the higher the likelihood that he will make a purchase.
What is lead scoring for?
First and foremost, lead-scoring technology allows sales departments to not waste their time and focus only on those customers that are potentially beneficial to your business.
It sounds tempting, but in order to introduce lead-scoring, conditions are needed:
Condition # 1
Sales team. First of all, you should have a sales team that works directly with the leads.
Condition # 2
Analytics. You collect data about your users, including demographic information and behavioral indicators.
If these conditions are relevant for your product, I would recommend you to think about introducing lead-scoring, because lead scoring is about efficiency.
How to start working with Lead-scoring?
Portrait of person
The starting point of lead scoring is an understanding of the target audience and a clear view of people. I am sure that for any formed business there is an understanding of the target audience. But no matter how detailed it is, before starting work on lead scoring, I recommend talking to the sales team. It is as close to the context as possible and knows who and why is being converted.
You will learn a lot of additional information that will enrich your view of the target audience. This will be the basis for working with the analyst, on the basis of which you will form the logic of lead scoring.
Behavioral indicators
Lead scoring is built (and this is its very important component) on behavioral indicators. This is a combination of both qualitative characteristics and interactions with your product at the site or application level.
The task of lead scoring is to select from the entire flow of incoming leads those that fit the target audience, and from this segment to select those who, through their behavioral indicators, confirm that they are interested in your product. Here is the main point on which your sales managers should keep their focus.
How does lead scoring work?
When working with data and analytics, it is important to validate your perception of the target audience. Find additional insights, key actions that suggest that users who are characterized by these parameters or actions are more likely to be converted.
The first step is to calculate the total conversion, that is, the total sample of your users and what percentage of them reach the target action. For example, if we are talking about the standard model of free trial subscription, then we calculate the percentage of users who signed up after the trial.
The second stage is the same stage of working with analytics, when you try to find the key actions that are characteristic of leads and segments of leads with a higher conversion than your average conversion in the whole product.
And the third point is the essence of lead scoring. For each quality characteristic and action, you must accrue a score. But the following is important here: understand how valuable each feature and action is in relation to other actions and characteristics that your users have.
Experience of using lead-scoring in PandaDoc
PandaDoc has a standard model for SAS products. We have a free 2-week trial, after which users must subscribe if they want to continue using the product. Within our model, the overall average conversion is considered to be the construction of the simplest funnel:
create an account, start a free trial; b) subscription.
Suppose that the average conversion for the whole product over a certain period of time is 7%. Then our task is to segment the total sample that we have formed, and see what the conversion of each individual segment is. You can segment at the level of absolutely all the characteristics that you know about your users: traffic sources, demographic indicators, and so on.
I will give a few examples of such individual conversions that will help you figure out what we are looking for. Suppose I want to split the entire sample of users based on the industry. At the level of our platform, we ask incoming users “what industry does their business belong to?”. The task is to understand, and which industries, which segments on the basis of industries are converted better or worse than our average.
Creative / MarketingAgency, for example, is almost twice as good at converting - 13.3%. In the context of lead-scoring, this means the next time a user comes to us who has such a characteristic, we will award him points for having such a characteristic. We do this in order to differentiate and isolate the user from the total sample, since he has a qualitative characteristic that increases the likelihood that he will become our paid user.
The question is: how many points to charge for the presence of this characteristic? Different approaches are used here, but the simplest one is that you can score as many points as the conversion of this segment into the target action. In this case, the conversion on the example of 13%. You can charge 13 points for this action. This is an example of segmentation and search for key parameters at the level of the quality characteristic.
As we said, lead-scoring is also based on user actions and behavioral indicators. In the same way, you can break up the entire sample of your users and watch how users who have taken an individual action are converted.
You might assume that visiting the pricing page on your site is good. If a user went there, the probability that he is interested in the product and will issue a paid version is higher than those who did not go there.
Without analytics hard to understand. If you build such individual conversions, you will find very interesting data that you could not even suggest. In this example, we could find that visiting a pricing page results in almost 30% of users converting to paid. What does this say in terms of lead scoring? This suggests that the next time when one of the users performs this action, we will give him 30 points, so that this user is selected and he is on the top for the sales department.
Not always and not only on the basis of individual actions, it is necessary to build the logic of lead scoring. This is quite a creative process, and I would advise you to approach it accordingly. You have to generate hypotheses and look for different, including combinations of actions and characteristics, which, in your opinion, can say that the segment of these users is very high quality.
Through the simplest funnels, you can find out what the conversion is for a segment that has a combination of characteristics and various actions. I guarantee you that you will find segments simply with space conversion.
Total
The lead scoring in PandaDoc consists of more than 180 rules. These are various parameters, actions and their combinations, for which we award points to users. We use Hubspot, a third-party product that, among other things, allows you to customize the lead scoring logic. And the third in all this history is important threshold value skora.
We have a website, a web application, Hubspot and a CRM system (this is the main product for our sales department). Imagine that three users come to our site: Margi, Betsy and Adam. Two of these users create an account in PandaDoc. As soon as they come to our site, we begin to track where they came from and what actions they performed on the site. As soon as they get into our application, we create profiles of these users in Hubspot, where all the information about each specific user is stored. Hubspot analyzes and sets the corresponding score for each individual user.
Further integration is configured between Hubspot and the CRM system, and there is a threshold value here. We do not send all leads to our CRM system. We do this so that our sales department does not lose focus and does not look at those who do not represent a product of great interest.
At the moment, first, with the threshold value you have to decide for yourself, this is a very individual characteristic. For us now (for general presentation) is 50 points. Only leads with 50 or more points go to work with our sales department. In this example, Adam has 75% in accordance with all characteristics and actions.
What does this mean for our business? This is what Adam gets into the sales force. In the CRM system, by its own internal rules, it falls on the right sales manager, who works with clients from certain industries, works with companies of the appropriate size, etc. And our sales manager calls Adam and starts working with him directly.
Margi 42 points. She misses the sales force at the moment. It is not necessary that she never receive a call from our sales department. Lead scoring is a dynamic indicator. It is constantly updated, and if Margi, in turn, continues to use our application, perform some key actions, use important functionality, which indicates that she is strongly interested, the score will increase. And as soon as she reaches the threshold value, she gets there. There are users who will never get there, as this is a defocus for the sales team.
Lead Scoring Tips:
1. Iteratively refine the lead-scoring logic. After the introduction of this technology, it is important to analyze the process and results. Analyze all those leads that get a high score. View those who do not receive them. Look for those that got a low score, although in fact you see that they are quality leads.
2. Use negative scores for negative parameters and actions. In fact, in lead scoring it is still important to give negative points to those segments of users that you are not interested in.
3. Earn points based on the financial metrics of each individual segment. In particular, LTV.
Epic Growth Conference is a product marketing conference organized by Mobio , Getloyal and Appsflyer with the support of myTarget , Appnext .
Subscribe to the Epic Growth Channel in the Telegram, and get interesting content on product marketing.
Decoding performance on VC .
Watch the video and read the decoding under the cut.
In general, I will tell you how to enable the sales department to be more efficient and to multiply the growth of your product.
')
What is lead scoring?
Lead scoring is the methodology for evaluating and ranking each individual lead according to the interest it represents for your business. In simple terms, the method of segmentation of potential customers who are in the sales funnel.
Under this methodology, you score points for every perfect lead action and for every quality characteristic he possesses. For example, where did the lead come from, what company does it represent, what position does it take, what actions has it done inside your application. The more points credited to the lead, the higher the likelihood that he will make a purchase.
What is lead scoring for?
First and foremost, lead-scoring technology allows sales departments to not waste their time and focus only on those customers that are potentially beneficial to your business.
It sounds tempting, but in order to introduce lead-scoring, conditions are needed:
Condition # 1
Sales team. First of all, you should have a sales team that works directly with the leads.
Condition # 2
Analytics. You collect data about your users, including demographic information and behavioral indicators.
If these conditions are relevant for your product, I would recommend you to think about introducing lead-scoring, because lead scoring is about efficiency.
How to start working with Lead-scoring?
Portrait of person
The starting point of lead scoring is an understanding of the target audience and a clear view of people. I am sure that for any formed business there is an understanding of the target audience. But no matter how detailed it is, before starting work on lead scoring, I recommend talking to the sales team. It is as close to the context as possible and knows who and why is being converted.
You will learn a lot of additional information that will enrich your view of the target audience. This will be the basis for working with the analyst, on the basis of which you will form the logic of lead scoring.
Behavioral indicators
Lead scoring is built (and this is its very important component) on behavioral indicators. This is a combination of both qualitative characteristics and interactions with your product at the site or application level.
The task of lead scoring is to select from the entire flow of incoming leads those that fit the target audience, and from this segment to select those who, through their behavioral indicators, confirm that they are interested in your product. Here is the main point on which your sales managers should keep their focus.
How does lead scoring work?
When working with data and analytics, it is important to validate your perception of the target audience. Find additional insights, key actions that suggest that users who are characterized by these parameters or actions are more likely to be converted.
The first step is to calculate the total conversion, that is, the total sample of your users and what percentage of them reach the target action. For example, if we are talking about the standard model of free trial subscription, then we calculate the percentage of users who signed up after the trial.
The second stage is the same stage of working with analytics, when you try to find the key actions that are characteristic of leads and segments of leads with a higher conversion than your average conversion in the whole product.
And the third point is the essence of lead scoring. For each quality characteristic and action, you must accrue a score. But the following is important here: understand how valuable each feature and action is in relation to other actions and characteristics that your users have.
Experience of using lead-scoring in PandaDoc
PandaDoc has a standard model for SAS products. We have a free 2-week trial, after which users must subscribe if they want to continue using the product. Within our model, the overall average conversion is considered to be the construction of the simplest funnel:
create an account, start a free trial; b) subscription.
Suppose that the average conversion for the whole product over a certain period of time is 7%. Then our task is to segment the total sample that we have formed, and see what the conversion of each individual segment is. You can segment at the level of absolutely all the characteristics that you know about your users: traffic sources, demographic indicators, and so on.
I will give a few examples of such individual conversions that will help you figure out what we are looking for. Suppose I want to split the entire sample of users based on the industry. At the level of our platform, we ask incoming users “what industry does their business belong to?”. The task is to understand, and which industries, which segments on the basis of industries are converted better or worse than our average.
Creative / MarketingAgency, for example, is almost twice as good at converting - 13.3%. In the context of lead-scoring, this means the next time a user comes to us who has such a characteristic, we will award him points for having such a characteristic. We do this in order to differentiate and isolate the user from the total sample, since he has a qualitative characteristic that increases the likelihood that he will become our paid user.
The question is: how many points to charge for the presence of this characteristic? Different approaches are used here, but the simplest one is that you can score as many points as the conversion of this segment into the target action. In this case, the conversion on the example of 13%. You can charge 13 points for this action. This is an example of segmentation and search for key parameters at the level of the quality characteristic.
As we said, lead-scoring is also based on user actions and behavioral indicators. In the same way, you can break up the entire sample of your users and watch how users who have taken an individual action are converted.
You might assume that visiting the pricing page on your site is good. If a user went there, the probability that he is interested in the product and will issue a paid version is higher than those who did not go there.
Without analytics hard to understand. If you build such individual conversions, you will find very interesting data that you could not even suggest. In this example, we could find that visiting a pricing page results in almost 30% of users converting to paid. What does this say in terms of lead scoring? This suggests that the next time when one of the users performs this action, we will give him 30 points, so that this user is selected and he is on the top for the sales department.
Not always and not only on the basis of individual actions, it is necessary to build the logic of lead scoring. This is quite a creative process, and I would advise you to approach it accordingly. You have to generate hypotheses and look for different, including combinations of actions and characteristics, which, in your opinion, can say that the segment of these users is very high quality.
Through the simplest funnels, you can find out what the conversion is for a segment that has a combination of characteristics and various actions. I guarantee you that you will find segments simply with space conversion.
Total
The lead scoring in PandaDoc consists of more than 180 rules. These are various parameters, actions and their combinations, for which we award points to users. We use Hubspot, a third-party product that, among other things, allows you to customize the lead scoring logic. And the third in all this history is important threshold value skora.
We have a website, a web application, Hubspot and a CRM system (this is the main product for our sales department). Imagine that three users come to our site: Margi, Betsy and Adam. Two of these users create an account in PandaDoc. As soon as they come to our site, we begin to track where they came from and what actions they performed on the site. As soon as they get into our application, we create profiles of these users in Hubspot, where all the information about each specific user is stored. Hubspot analyzes and sets the corresponding score for each individual user.
Further integration is configured between Hubspot and the CRM system, and there is a threshold value here. We do not send all leads to our CRM system. We do this so that our sales department does not lose focus and does not look at those who do not represent a product of great interest.
At the moment, first, with the threshold value you have to decide for yourself, this is a very individual characteristic. For us now (for general presentation) is 50 points. Only leads with 50 or more points go to work with our sales department. In this example, Adam has 75% in accordance with all characteristics and actions.
What does this mean for our business? This is what Adam gets into the sales force. In the CRM system, by its own internal rules, it falls on the right sales manager, who works with clients from certain industries, works with companies of the appropriate size, etc. And our sales manager calls Adam and starts working with him directly.
Margi 42 points. She misses the sales force at the moment. It is not necessary that she never receive a call from our sales department. Lead scoring is a dynamic indicator. It is constantly updated, and if Margi, in turn, continues to use our application, perform some key actions, use important functionality, which indicates that she is strongly interested, the score will increase. And as soon as she reaches the threshold value, she gets there. There are users who will never get there, as this is a defocus for the sales team.
Lead Scoring Tips:
1. Iteratively refine the lead-scoring logic. After the introduction of this technology, it is important to analyze the process and results. Analyze all those leads that get a high score. View those who do not receive them. Look for those that got a low score, although in fact you see that they are quality leads.
2. Use negative scores for negative parameters and actions. In fact, in lead scoring it is still important to give negative points to those segments of users that you are not interested in.
3. Earn points based on the financial metrics of each individual segment. In particular, LTV.
Epic Growth Conference is a product marketing conference organized by Mobio , Getloyal and Appsflyer with the support of myTarget , Appnext .
Subscribe to the Epic Growth Channel in the Telegram, and get interesting content on product marketing.
Decoding performance on VC .
Source: https://habr.com/ru/post/359224/
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