ABM is pretty simple in terms of configuring it but the real challenge is when you try to decipher data that you see on your screen. Let's say you visit a vegetable market and you have a whole heap of vegetables and fruits in front of you, and you have to do the tedious job of selecting what you want from that heap. Whereas, if they are all segregated and shelved neatly, with the price labels for each of them, it makes the your life much easier, as you can pick what you want with ease, spending less time and effort and the seller can calculate the purchase easily. Similarly, when you have all the needed data segregated, you can engage your customers to the right path which will help the sales team to market to the right batch of customers and the customers need not have go through a whole of irrelevant marketing materials.
ABM offers you two things to simplify data deciphering for your accounts.
These metrics help you to segment customers into groups that make sense, understand them based on their past purchases, and engage them with more personalized campaigns.
In this ABM tool, the Accounts module (or any other module that represents Accounts in your configuration) will be segmented and the Deals module (or any module that represents Deals in your configuration) will contribute to the segmentation by default. You cannot alter this manually. The user can choose their criteria to manipulate the quotients of the variables R,F and M. Using the pattern of these quotients, the user can create labels to represent desired purchase behavior of the Account. The entire process of this configuration is similar to that of the one inside the CRM. A Maximum of 8 labels can be configured in this segmentation technique.
You must select the appropriate fields for the recency, frequency, and monetary values in your business and give each a score ranging from 1-5 for different levels of the RFM metric. For example:
If the last purchase date is last month, the recency score is 2.
If the total number of products purchased is 5, then the frequency score is 4.
If the total purchase amount is above $60,000, the monetary score is 4.
Based on the scores that you set, the system will compute the RFM metric for the record.
Recency
By default, score for each metric (recency, frequency, and monetary) is set between 1 to 5. You can decrease the range by dragging the score range slider to the desired position to set it to 1-2, 1-3, or 1-4. You can either set the score manually or allow the system to do it automatically.
Manual: You will manually define criteria so the system will know whether the R, F, and M scores should be 1, 2, 3, 4, or 5.
For example:
Set the recency score to 5 if the sale closing date was between this week and last week or was last month. Set the score to 4 if the most recent purchase time was before yesterday, and so on.
If the number of purchases is more than 8, the frequency score is 5. If it is between 5 and 10, the score is 4, and so on.

Set the monetary score to 5 if the amount spent is more than $1500, set it to 4 if it is between $800 and $1200, and so on.

Automatic: The system will automatically compute the RFM score using the percentile method. The entire data set will be divided into five parts (so that scores can be set within a range of 1-5. That is, the RFM scores will be given as 1, 2, 3, 4, and 5), with distribution at every 20th percentile. The RFM scores range from 0 to 20, 20 to 40, 40 to 60, 60 to 80, and 80 to 100 on the percentile scale.
The data set will be sorted in order. For example, for the recency score, the data set will be sorted from the most recent purchase to the oldest purchase. For example, if the most recent purchase was a week ago and the oldest purchase was 6 months ago, then CRM will set these two timelines as the boundaries and draw the percentiles within this range. For frequency, the data set will be sorted from most frequent to least frequent, and for monetary it will be sorted from the highest amount to the lowest.
Segment Label Criteria: If a technique's label is used in creating a segment, the label criteria cannot be modified. A warning popup will notify users of associated segments tied to the label.
Firmographics
The user can segment his account based on the firmographics metrics. These metrics will help gauge the Accounts better using market terminologies. These metrics include industry type, revenue generated, status of the organization, number of employees etc., These metrics can be fetched from the fields map during configuration. These metrics can be used as a criteria to create labels. The accounts matching the criteria will be associated to a particular label. The user can create a maximum of 5 labels and a minimum of 2 labels.
Labeling the segments
You can create different labels based on your customers' firmographic metrics to help other teams understand each customer's position in the sales cycle and take appropriate actions. Each segment can have up to 5 labels and ABM for Zoho CRM offers a set of predefined labels. You can edit these predefined labels or delete them and add new labels.
Follow the steps below to add a label:
- Click Add label.
- Enter the label name and the criteria for the label.
- Click the plus icon to add another criteria to the label, if required.
- You can associate criteria using AND / OR operators.
- Click the right icon to confirm the criteria for the label.
- Similarly, you can add upto 5 labels for the firmographics metrics to sort the accounts using market terminologies.
Engagement
Interaction with the account and engagements across environments and channels contribute a crucial part in segmenting an account. Account engagement suggests the rapport between you and the account.
The sales team uses various methods to contact accounts and each contact is given a score based on their responsiveness. For example, a response on Facebook will give 5 points, an answered phone call will give 6 points, an email response will give 6 points, and so on. All these points add to give a total score to the account, which helps the sales team to identify their level of interest and prioritize them accordingly. If a contact is not responsive during engagement, their score will indicate the exact touch point the sales personnel needs to work on. For example, if the customer does not answer a phone call, their score will fall. The falling score can indicate that the time the call was made may not be convenient for the contact. The rep can check the best time to get hold of the contact for a favorable response.
You can calculate engagement score by enabling various channels used for account engagement in your ABM.
Label Configuration
You can create labels to segment your accounts based on engagement score. You must add a minimum of 5 labels.
The score range for each label is set between 0-100. You can reposition the slider to change the score range.
Recommendations
Recommendation technique in ABM uses artificial intelligence to identify and analyze customer data such as their purchase details, interests, requirements, and behavioral patterns to suggest the most relevant product. It also compares the behavioral patterns of other customers with similar attributes to recommend the right product or service.
With the help of a ABM recommendation, you can bring a significant difference to the overall revenue, sales, purchases, conversions, click-through rates, cross-selling, and up-selling in your business.
Furthermore, you can use the suggested results to modify every aspect of your marketing activities.
ABM recommends the right product for the right user at the right time by understanding the behavioral pattern of the entire customer base. We have outlined some common areas where you can use the ABM recommendation to pitch the right product to your customers:
For example, let us take the online learning platforms in the education industry. Students who have subscribed for a particular course can be recommended similar courses based on the topic they have subscribed for, personal preferences and preferences of other students with similar characteristics. Based on the above results, you can suggest related materials such as interactive videos, assessments, supplementary education materials etc. to ensure better engagement. ABM recommendation lets you recommend more courses based on the current course content. For example, if a student has subscribed for Foundation Course on Machine Learning, you can suggest other courses that have related content like Deep Learning, Neural Networks, Regressions etc.
Types of recommendations
ABM recommendations are categorized as:
First Buy - Depending on the buyer's first purchase ABM will recommend the appropriate product.
Next Buy - ZIA recognizes the purchasing sequence of products and depending on the buyer's second purchase ABM will recommend the appropriate product. Let's say some accounts bought products in the following order:
Product 1: Basic Java course
Product 2: Core Java course
Product 3: Advanced Java course
Now, for any new account that acquires the Core Java course, ZIA will suggest the Advanced Java course.
Repeat Buy - ABM will identify the product or service that is repeatedly selected by the buyer and will recommend the repeat buy to the user. For example, a food and grocery chain can identify the customer who frequently purchases the same items and suggest the same to prevent discontinuity in the purchase cycle.
Cross-sell - ABM identifies connections between products and provides recommendations based on those relationships. For example, let's say you're shopping online for a new smartphone. After selecting a smartphone, the website suggests related products, such as phone cases, screen protectors, and headphones. These product recommendations are based on the idea that if you're buying a phone, you might also be interested in accessories that complement it. This is an example of cross-selling, where related products are recommended to enhance your shopping experience and potentially increase your purchase.
Bundle - If any buyer purchases multiple items together such as mobile phone, screen guard, and mobile cover. Then, Zia will consider this as a bundle and recommend bulk purchase if anyone has selected either of the products from the bundle.
Label Configuration
Using the recommendation types and the purchase probability, you can create labels to segment your accounts. By default, ABM Recommendations has two labels, hot prospect and cold prospect.
Hot Prospect: Potential customer who exhibits strong interest, high engagement, and is likely to convert into a paying customer soon.
Cold prospect: Potential customer who shows little to no interest or engagement, and is less likely to convert into a paying customer without further nurturing and targeted efforts.
You can add upto five labels to the technique and a minimum of 2 labels.
The general configuration for the Recommendations technique is derived from Zoho CRM.
Recommended module: Module that contains information about what you want to recommend for example, deals, products, services etc.
Recommended on module : The module that contains end user or customer information. For example, if you want to recommend courses for customers, leads or contacts, the latter's information must be in the recommended on module.
Transaction module: A module that contains information about recommended and recommended on module is an interlinking module. It is a mandatory module because it will help Zia determine the successful transactions that take place in your business.
For example, Deal is the connecting module for Product and Contact modules, because it has all the information about the purchased products, date of purchase, type of customers who choose a particular product, other products that may have been purchased by them, type of products preferred by a group of customers.
Duration: Indicates the completion state of a process. For example, purchased on, subscription renewed on, deal closed on etc, they indicate successful completion of a sales process. This will help Zia learn the frequency of purchases and recommend those items for repeat purchase, next purchase or items to be bought together etc.
Only those modules that are interrelated to the source module through a lookup field or related list can fetch relevant data for the recommendations technique. This is nothing but the initial module mapping that we did for ABM configuration.Therefore, we need not do any manual configuration for the recommendations general setup.
To create a label:
- Click Add Label on the Recommendations page.
- In the Label configuration page that appears, enter a label name in the Label name text field.
- In the Preferences section, you can choose any two or more of the recommendation types.
- Select the products that you would like to associate with each recommendation type and set the purchase probability score for the same.
Purchase probability score is a quantitative measure that assesses the likelihood of a prospect or customer making a purchase.
- Click Done to save the label configuration.
- You can edit or delete a label anytime required by clicking the Edit or Delete icon, respectively against each label.
1. In the First Buy, Next Buy, re-buy, and cross-selling, you can select up to a maximum of 10 products.
2. When it comes to bundles, you can add only two bundles, with each bundle accommodating two products.
3. You can edit or delete a label only twice a day.
Export segments from ABM