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Posts Tagged ‘CRM’

How to map your SaaS sales process

January 20, 2013 1 comment

A sales process does not have a single path. It is not a simple, one time, sequence of events. A sales process from lead to close may go zig zag, back and forth, and in circles. Also, the customer/partner may interact with multiple touch points like, pre sale, tech support, and billing. It is not enough to just develop a single dimension sales funnel with steps going one after another from the wide top opening of the sales funnel to the narrow bottom end. In order to identified who is doing what, and when, where leads may fall between the cracks, and where there are opportunities to up-sale, I recommend mapping the sales process in the following way.

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Step 1: Leads generation – the list of marketing activities that will generate leads/contact

Step 2: Referrals –  identify where are they coming from

Step 3: Adding to the leads bank – some filtering/de-duping can happen here. The number of active leads is the denominator for the conversion and close rate calculation. This is where Sales and Marketing needs to work together and agree about what counts as a lead. The information needs to be time sliced – usually on a quarterly basis. Some leads that did not progress to the next step in the last quarter, despite multiple follow-ups, can go back to this pool for the next quarter. Others may be stored for a longer term follow-up.

Step 4: The volume of leads to follow-up depends on the amount of leads available and the inside sales capacity. So, here too, some leads need to slip to the next time slice. It is a good practice to keep an eye on the % follow-up = # of leads contacted # of leads available, to see that sale reps keep prospecting.  The next step after following up with a lead, is getting the prospect to perform certain activity(e.g. registering to the web-site, downloading a white paper). This step split the process in two: 1. The lead is now qualified, showed interest and maybe other resources from the company to follow-up with her. 2. The lead did not react to the offer and it should be parked in the “to follow’ up pool of leads.

Step 5: Close – the prospect is on-board and productive. Here it is good to calculate the close rate (could be done from the total # of leads or just from the leads that the sales reps contacted).  Here again, the prospect can go to the “to follow’ up pool of leads if the deal was not closed. Also, another base practice, and this is crucial for SaaS company, is calculating the renewal rate to monitor retention. Resigning customers should be added to the “to follow’ up pool set with high priority, and should be called as soon as possible. On-boarding is a crucial step so monitor the retention rate relative to new customer in addition to the overall customers base renewal rate.

Step 6: Closing up-sale opportunities. This is where other customer touch points possibly contribute to higher close rate.  Calculating the close rate here could be tricky. It is not simply calculated based on the # of leads from step 3, but also taking in consideration the entire customer base. One of the key question that SaaS companies struggle with is how to identify opportunities for up-sale. This is where segmentation becomes crucial, and foresight thinking about what data to collect about the customers is the key.

One more thing to consider, referrals to generic leads are like jet fuel to the gas you fuel your car with. There is no question whether to invest in referral program, but it is hard to know how much. Here too, the data you collect about your referrals can help you to come up with the right answer.

In summary, the sales process looks more like a ping ball machine than a sequence of inputs and exits criteria. If your sales process is even more complicated than my example above then it is even more critical for you to spend the time mapping it.

Mapping the sales process can help with:

    • Identifying where leads should be followed-up, and by whom in the organization
    • Where leads should go back to the short term or longer term pools
    • Where and what data should be collected along the way
    • To identify opportunities for up-selling

This exercise can go a long way increasing your you revenue as a result of an increase in your close rate.

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Datanetis – highly scalable solution for finding influencers mathematically

December 28, 2008 1 comment

  I recently noticed on LinkedIn that one of my old friend is now the CEO and founder of a new start-up company named Datanetis. I sent him an email because I was intrigue and it was also a good opportunity to catch up.  Elery Pfeffer and I worked together few years ago in a small start-up called Nester Software (later the name changed to Plataine) he was a student back then and I was thinking about moving to America. He is one of the brightest people I ever met.  Elery graduated from Tel-Aviv university in computer science and in a country with so many bright people and very few universities, it is as hard to become a student in Tel-Aviv university as it is in one of the Ivy League education institutions over here. Yet the thing that makes Elery a great friend is his strong integrity and generosity. Later he become the President of Nester, I made it to the state and we kept the friendship and mutual respect going.

Elery was kind enough to spend an hour with me over the phone and through GoToMeeting session he presented his new creation. Now, I was even more excited. Datanetis is the real thing! It is not another dot com web2.0 bubble company. The company has a real product with multiple patent pending applications describing algorithms for finding influencers, highly complex (real barriers to entry), and scalable. Datanetis is selling it to the enterprise as a hosted solution and providing new data about whom should the bushiness focus his best marketing effort on – leads to influencer on other leads. And they already have large customers around the world.

Elery view of the new marketing is revolutionary.

“The new marketing is not just about customer’s monetary value but also about the customer’s social value to the organization.”

For someone that has been working building software for the marketing automation industry over 8 years now and is familiar with multiple solutions for finding the right prospect out of many, it was an eye opener. I’m evidencing the progression from mass email campaigns through marketing to target individuals with a matching/relevant offers (data mining, behavioral pattern, collaborate filtering, recommendation engines) to finding customers that can market for you – agents.

Finding subtle connections between individual and causality.

Followers buy only in networks where influencers buy first

Quoting Harvard Business Review:

“The only path to profitability growth may lie in a company’s ability to get its loyal customers to become, in effect, its marketing department”

So who are these loyal customers?

Datanetis’s software is capable of both automatically generating social networks from low level CRM data within the customer database and to mathematically identify influencers and followers. This information could be used to increase the return on every marketing dollar spent on new product adoption(x5-x70), new customers acquisition(x12), churn prevention (x10-x30), conversion, product cross sell(x6), higher product virality, and significant cost reduction.

From Datanetis’s experience, social network marketing using influencers is comprised of two cycles. The first is the closed friends cycle, from the results it seems as if they almost decide simultaneously to follow the leader. This first wave peaks around 4-5 weeks from the beginning of the campaign. The second wave peaks around 9-10 weeks traversing through the rest of the social sphere. This may indicate that the customers are going through different decision making processes at different locations on the social network graph (influencers affinity). This knowledge offers Datanetis customers ways to fine tune their campaigns over time.

In my opinion this disruptive technology will force changing some of the operations and thinking in the marketing department going forward.

Viral marketing is not just hit and run but a multiple acts campaign.

Another interesting finding that Elery shared with me is the lack of influencers trivial characteristics. Any attempt, so far, trying to map influencers based on demographic, product adoption or any other factor distinguishing them from the population failed.

This is what Datanetis found about these individuals only after finding them using their software:

  • Social Influencers are not celebrities and resellers
  • Influencers typically
    • represent 7-15% of the total population
    • has influence in 3-5 different subject areas

Datanetis successfully executed hundreds of large scale social marketing campaigns and is globally active in Retail, Telco, Gaming, Internet, and Hospitality industries.

Here is what that Elery suggests to the marketers out there:

“Don’t focus only on turning leads into sales, focus on turning influencers into ambassadors for your company.”

The influencers phenomenon is covered vastly on the web ever since the invention of digital social networks and the social graph. From what that I read so far it seems to me that we are still in the experimental/research phase. I was excited about finding a real application with proven method for finding influencers. Maybe Datanetis will help pushing this new science forward.