We recently chose to have a focus on offering productized Sales Operation consulting solutions. We got our first client and within four months, we reduced their sales team cost by 20% and added $6MM to their projected revenue. Our machine-learning-powered solution delivered more than 4MM sales leads with verified contact information to them and cleaned 97% of the duplicates in their Salesforce CRM. That helped them increase the number of valid sales leads by 400% and increase their 20% YoY growth rate even further. The results were so promising that they already have asked us to provide them with a few more solutions including lead scoring and churn prediction.

It is the right time to focus on investing in technology enablement solutions for Sales and Sales Operations teams. Why and how? Please keep reading and you will find out.

Today, Sales and Sales Operations are where Marketing was a decade ago, ready for disruption by data driven approaches and solutions. Data driven methods and machine learning (ML) have changed the game in marketing in a drastic way; to the extent that one has a narrow chance to get a job in Marketing if at least they don’t have a basic understanding of data analysis. Marketing has become a way more efficient market compared to even 5 years ago. Today, less sophisticated players even with an innovative and on-demand products/services find it extremely hard to deliver their messages to their audience. The last time I checked (August 2018), there were around 8000 companies making technology products to help marketers stay competitive. Sales and Sales Operation are next.

Vast amount of data is gathered by sales teams and other teams every day and it has invaluable amount of information and insights in it. There are numerous inefficient or tedious processes in sales and operations that can be handled by machines. It’s not about replacing humans, it’s about enabling them to do their job and what they are great at more and spend less time and energy on tasks that could be done using machines or in other words by cheaper capitals. It’s about empowering sales teams with insights that are hard for us as humans to obtain but computers can do in a fraction of second.

Now, let me give you a few ideas on how you can hop on this fast-moving train and not be left behind. Here are a few ways you can use data science and ML to take your sales/sales operations to the next level, easily increase your revenue at least by few percent and reduce your costs by another few percent. Not only these solutions improve your business KPIs, they also cause your teams focus on what they really need to do and do what they enjoy the most and become happier in their work.

· Dedupe/Clean Up Your CRM

· Enrich Your Data

· Automate Your Lead Generation

· Score Your Leads

· Cross-Validate Your Leads

· Right Lead for the Right Person

· Reach Them at the Right Time and Place

I’m going to write a detailed article on each of the above and other subjects in the coming weeks so stay tuned! For now, let me give you a short description of each subject.

Dedupe/Clean Up Your CRM

CRMs are the heart of any company. Based on our experience, 10–20% of the records on CRMs are duplicates. That’s 10–20% waste of your budget if you send them a postal card for example adding up to millions and millions of dollars for larger companies. And for your sales reps, it’s 10–20% of their time wasted and constant frustration.

Enrich Your Data

Do you treat a senior person the same way your treat a younger lead? When your reps are cold calling a lead, should they be talking about your solutions being on cloud or emphasize on ROI more? Have your leads been in business for a while or just started? Did they just raise a big round or have been financially struggling for a while?You don’t know if all you have is a name, email or phone number. Nowadays, just by looking up their email, looking at their social media and etc.; we can get you a lot more about someone. Age, gender, music taste, you name it! Knowledge is power.

Automate Your Lead Generation

The leads you bought from source X or paid a person in India to grab off the internet are not that reliable. You need to keep them updated and the only way you can do it is by having access to a resource that can do it for you regularly. However, it’s a repetitive task that shouldn’t cost you every time you do it. You need a smart bot to do it for you on a regular basis.

Score Your Leads

Leads have different qualities and even leads with the same qualities have different chances of conversion. Why should you treat them the same or spend the same amount of time (in other words money!) to convert them? After enriching your lead data and knowing more about them, ML can accurately predict the conversion chance. In the long run, that means millions of dollars increased revenue, happier sales staff and happier you!

Cross-Validate Your Leads

Why would you spend your time and money from leads information that is coming from a single source? We’re no longer in 50s. Everything has a higher speed, people move around and there’s an enormous amount of bad data out there. By cross validating your data with several sources, you are improving your leads quality sometimes by a factor of 10. Guess the result? Millions of dollars increased revenue, happier sales staff and happier you!

Right Lead for the Right Person

Sales reps are human and have different personalities, capabilities and interests. Sally might work better with more senior leads and Alex might have more chemistry with a young lawyer who just opened their practice. By looking at your data, it’s no longer a guessing game or gunshot approach. You give me the leads I have a higher chance of closing. The result? All of the good stuff above and higher predictability of revenue.

Reach Them at the Right Time and Place

Mondays are not great for reaching out to leads, right? Nobody reads their email on weekends, right? Depends! When I was raising capital for my previous company, I found out the best time to contact more senior investors is on weekend evenings. Based on a project, we found out the best time to approach women in a specific demographic is Monday mornings. Email versus phone vs etc.? Depends! The good news is that it’s all hidden in your data you already have. The bad news is that you need to know how to extract it or it’s just sitting there.

Can you afford your competitors benefit from these superpowers without you taking any steps?

Thanks for reading this and looking forward to writing more about this topic in the coming weeks. Sign up for our newsletter, to get our articles straight in your inbox.

We asked one of our clients, a well respected and rapidly growing company in the Vancouver Sales Vertical what impact our AI engine had on their Sales Operations. They told us that within four months we had reduced their sales team cost by 20% while at the same time adding more than $6 million to their projected annual revenue.

We were quite taken aback. Initially we expected a more modest impact as our Machine Learning Solution was relatively new and our algorithms were not fully trained. In fact we had not even begun training our model to predict churn and lead scoring.

This feedback led us to reach out to other firms like Maximizer, FreshBooks, etc and when we asked them about their toughest challenges in their sales and sales operations, we consistently heard the same messages:

• Duplicates in their CRM systems were causing them frustration and the lack of verified contact information was wasting valuable prospecting time.
• There was unhappiness with having to scale up a sales team when it was possible to enable already trained staff to work more effectively.
• Many leaders mentioned to us that although there was a surplus of AI solutions for marketers, they were disappointed that so few effective machine based learning solutions existed for sales and sales operations.
• Others commented that they were frustrated at managing vast troves of data knowing that they were missing out on key insights and having to spend way too much time managing their ever growing data piles.

Over the next few weeks we will explore deeper into these topics. Sign up for our newsletter, as we share our findings around:

• What parole applications can teach us about timing in sales.
• How to calculate the real costs of CRM duplicates
• How AI can enrich your data beyond basic demographic data
• Which smart-bots work best with Machine Learning to optimize your lead generation
• Why a 5% increase in your lead scoring is the best thing to accomplish this week
• How correctly matching a lead to the right salesperson makes for happier customers and a more engaged sales force.

Stay tuned!

We recently helped a client to clean up their Salesforce CRM. First, we made our proprietary solution to find all of the duplicate records that would pass the client’s criteria and then had to use Salesforce native merge functionality to remove the duplicates. By doing the deduplication process, we helped them save their Sales Reps time by an estimated 10% to 20%.

After a few hours of searching online, we figured that there is not much documentation on how to interact with the Salesforce API we had to work with, their SOAP API and particularly no one had talked about using their merge functionality. This article is written to make it easier for other developers that are planning to do what we did and save them time, a lot of it. Let’s say Salesforce API and SOAP APIs are not the most fascinating APIs to work with!

Our Programming Language:

You can send a SOAP request through any programming language that you like. Here, we are using python as it is becoming more and more popular especially among data scientists and other data related experts. Extending it to your favourite language should be quite straightforward though.

A Bit of Background:

Let’s look at a bit of background on SOAP protocol that can be found on Wikipedia:

“ SOAP (abbreviation for Simple Object Access Protocol) is a messaging protocol specification for exchanging structured information in the implementation of web services in computer networks. Its purpose is to provide extensibility, neutrality and independence. It uses XML Information Set for its message format, and relies on application layer protocols, most often Hypertext Transfer Protocol (HTTP) or Simple Mail Transfer Protocol(SMTP), for message negotiation and transmission.”

So essentially, we need to send an xml message to Salesforce API that is based on a given standard. You need to look at the Web Service Description Language of Salesforce (WSDL) to (hopefully J) find out how to format your xml message. The good news that we’ve done this for you for merging.

Let’s Get Started

First, we need to make a nice header to send with our API call. Here is how the header should look like:

headers = { 'Content-Type': 'text/xml', 'Accept': 'application/soap+xml,application/dime, multipart/related, text/*', 'Authorization': 'Bearer ' + self.session_id, 'SOAPAction': 'merge', 'Sforce-Auto-Assign': 'false', 'charset':'UTF-8'}

It should be quite self-explanatory; if you are wondering about ‘Sforce-Auto-Assign’: ‘false’ part, it’s helping us to avoid any weird default assignment rule that your Salesforce might have.

How about the body?

Now it’s time to actually make a nice xml message. We are doing a bulk merge request and hence we have a list of records that we want to merge. We have stored that list into a list called data and each row of data is a list containing the following in order:

• sf_type: type of the records to be merged (Account, Contact, Lead)
• master_id: Salesforce Id of the master
• victim_id: Salesforce Id of the victim
• valuesToKeep: Please see below

For example, the first row of data can be:

['Lead', '00Q2G00001atpfx', '00Q2G01001atqfl', {'Industry': 'Law', 'Phone': '+1 111 111 1111'}]

One very important note. Salesforce does not keep the info from the victim even if the fields for the master are empty! You need to tell it explicitly to overwrite those fields. As a result, we made a dictionary ( valuesToKeep) of field-names (keys of valuesToKeep) and victim values (values of valuesToKeep) we want to overwrite into the master record. For example, in the above, we are asking Salesforce to use ‘ Law’ as the final record’s ‘ Industry’ and ‘+1 111 111 1111’ as ‘ Phone’.

Finally, let’s put all of these together and form xml body:

body = '''<?xml version="1.0" encoding="utf-8" ?> <soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns:urn="urn:enterprise.soap.sforce.com" xmlns:urn1="urn:sobject.enterprise.soap.sforce.com" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><soapenv:Header> <urn:SessionHeader><urn:sessionId>{sessionId}</urn:sessionId></urn:SessionHeader> </soapenv:Header><soapenv:Body> <ns1:merge xmlns:ns1='urn:partner.soap.sforce.com'>'''.format(sessionId = self.session_id) for rec in data: sf_type, master_id,victim_id,valuesToKeep = rec body = body + '''<ns1:merge><ns1:masterRecord> <ens:type xmlns:ens='urn:sobject.partner.soap.sforce.com'>{sf_type}</ens:type> <ens:Id xmlns:ens='urn:sobject.partner.soap.sforce.com'>{master_id} </ens:Id>'''.format(sf_type = sf_type, master_id = master_id) for rec in data:
    sf_type, master_id,victim_id,valuesToKeep = rec
    body = body + '''<ns1:merge><ns1:masterRecord>    
    <ens:type xmlns:ens=
    'urn:sobject.partner.soap.sforce.com'{sf_type}</ens:type>    
    <ens:Id xmlns:ens='urn:sobject.partner.soap.sforce.com'
    {master_id}
    </ens:Id>'''.format(sf_type = sf_type, master_id = master_id)    for item in valuesToKeep.keys():
        try:
            value = valuesToKeep[item]
            body = body + '''<ens:{item}   
            xmlns:ens='urn:sobject.partner.soap.sforce.com'>
            {value}</ens:{item}>'''.format(item=item, value=value)        except:
            passbody = body + '''</ns1:masterRecord><ns1:recordToMergeIds>{victim_id}</ns1:recordToMergeIds> </ns1:merge>'''.format(victim_id = victim_id) body = body + '''</ns1:merge></soapenv:Body></soapenv:Envelope>'''

Let’s send our request!

We use the library requests to the API call. You are free to use another method that suits you.

import requests 
from simple_salesforce import Salesforcesession = requests.Session() 
sf = Salesforce(username = username, password = password, security_token = token, session = session, domain = domain)sf.session.request(method = 'POST', url = 'https://' + sf.sf_instance +'/services/Soap/u/' + sf.sf_version, data = body, headers = headers)

You need to provide for username, password, and security_token. domain is ‘test’ for Sandbox and None for the other cases.

Note: Salesforce API version 38 is problematic. You want to avoid it.

If everything goes well, Salesforce returns you a xml that contains no error message. Otherwise, it will try to help you understand where it went wrong, and you can correct any potential mistakes.

I wish you good luck and enjoy working with Salesforce API! Feel free to contact me if you had any questions.

The first time I felt the gap was during my PhD years when one of my close friends was looking for a job. He is a smart guy with great talent and a PhD in computational physics from a top university, UBC. He connected with many great companies whom really needed his expertise and had 5 successful interviews with each of them. However, they never hired him, as they thought he did not have enough job experience! They preferred to hire a candidate with a BSc with a couple of years of work experience than a recently graduated PhD. He started a tech company instead and after a year and half, had a successful exit.

There is a massive disconnect between leadership teams of companies who mostly come from non-technical backgrounds and the technical world, let alone academia. The gap is quite dangerous for the existence of these companies. We live in an era where technology plays a crucial role in companies’ success or ultimate failure. The leaders of a company need to have accurate understandings of the transition of the technologies used in their sectors and other sectors. They need to understand how they can benefit from the new technologies available and how they should plan for the future of their business, given that what used to be science fiction would be a reality soon.

In this post, the emphasis is on artificial intelligence (AI), machine learning, and data science and their potential contribution to a company’s success or failure. Until now the main beneficiary of AI has been the technology sector. However, more non-tech companies in a broad range of industries are starting to worry that AI could scorch or even incinerate them. In 2017, firms worldwide spent around $21.8 billion on mergers and acquisitions related to AI. Around 85% of companies think AI will offer a competitive advantage, but only 5% are “extensively” employing it today. This provides plenty of opportunities for the first-movers and substantial threat to others. McKinsey predicts that digital innovations including AI will provide an estimated economic impact of about $370 billion per year worldwide in 2025, so how would you benefit from this exponential opportunity?

Based on my personal experience, most business leaders have limited knowledge about the potential of AI for their business. Worse, their companies do not have experts who can provide consultation. Even the companies who have teams of data scientists and/or machine learning scientists cannot benefit from the knowledge of their team as the two sides live in very different worlds. Technical teams are mostly concerned about the details of projects and do not focus enough on the main concerns of the business team and the OKRs.

Now what can you do to make AI work for your business? McKinsey has a good report on this. I definitely suggest reading it. Here are some of my additional recommendations:

• Get an AI Advisor: Having advisors who are experts in a given field is a great way of making the right decisions. It is a common practice for startups to have advisors as they don’t have the resources to hire enough senior talent, but I believe it is a fantastic way for any company to stay competitive and informed about the new opportunities. There are many advisors who have strong technical backgrounds, but as a result of their business experience have learned the language of business teams. They can communicate effectively with the C-level team and even play the role of a bridge between the technical teams and the business teams.
• Attend/Organize Conferences/Meetups: No matter how experienced you are at your business, everyone can learn and it is really easy to learn new stuff in a more technical conference. Conferences gather a wide range of expertise with different ongoing projects. The speakers spend hours preparing for talks and the topics of these talks can give you a good sense of the technology trends in your industry and the other sectors. Meetups gather a more diverse set of attendees with different goals for attending the events. In addition to the usual networking opportunities and getting your brand out there, more technical meetups attract “nerdier” attendees, resulting in interesting conversations.
• Find an Expert Partner: You do not need to do it on your own. With so much interest in AI, it can still be really hard to attract good talent in that domain. As the demand for more hands-on consulting projects is increasing, more consulting firms are being born, which are focused on developing customized AI solutions. Think of them as an advisor that helps you to build the solutions in contrast to what McKinsey says. As not every company needs an IT department, not every company needs a team of AI/machine learning scientists.

Whatever you do, don’t sit still and start your AI transformation today.