People often think that the evolution of technology is a simple progress forward driven by small linear advances. The reality is always more complicated, looking at any technology and Sales technology’s in particular, progress resembles a
person walking after a few too many beers; trend intoxication sway it from side to side
while technology advancements push it forward.
To understand where Enterprise Sales
technology is headed, let’s look at the current trends and improvements defining it:
1. Pendulum trend swings:
outbound sales ->Inbound sales
-> outbound sales
2. Technology progress:
- Engagement –
In person -> phone & email
-> social & professional network
- Information – Static, manual CRM ->
real-time automated data
- Automation – linear formulas -> predictive analytics
More outbound, less inbound sales – Past few
years we’ve seen marketing departments take the first leap forward embracing
both automation and social technologies to generate more leads at a lower cost per lead.
This change on the marketing side caused a trend shift on the sales side of
responding to the new influx of leads coming from marketing (inbound leads) and
limiting previous outbound efforts in the form of cold calling on acquired
The nature of pendulum swings is that trends go to the extreme in one direction before
finding the right balance only to shift again later.
Entire enterprise sales team were formed under the new “inbound religion”
in which Marketing puts out sophisticated bates on multiple channels and reels
them in with marketing automation and all sales was left to do was close the
deal. No more prospecting, no more cold calling, no more targeted outreach.
The social greenfields and technological efficiency of marketing automation
provided the initial boost to support this change but shortly thereafter the
limitations became apparent. Main limitations were that inbound sales
works better with selling from the bottom-up while certain products
and certain organizations require a top to bottom purchasing process.
2015 has seen the rebirth of outbound sales. Even those “religiously inbound” sales teams are shifting resources back
to creating next-gen outbound teams working in parallel to the inbound teams. Some are even foregoing inbound efforts completely.
What are these new technologies that make outbound sales cost-effective again?:
Live social / professional
network data instead of static CRM data
Currently, most data in a company’s CRM
originates from marketing efforts and is updated manually by sales as they
learn new information or activity occurs. As we all increase our real-time
footprint on social and professional networks, this information makes finding
new leads and tracking their progress much more accurate. Managing a deal or
forecasting a quarter based on the old data in your CRM today is like driving
in high traffic based on a printed yellowing map you bought at the gas station
instead of using Waze.
Engaging with prospects on
multiple new channels instead of traditional phone/ email
The number of channels and apps we’re using to
communicate with other people has exploded in the past years. Back in 2005 the
average person used 3 communication channels (mostly a combination of
email, text & phone) while today they use 10 channels on average. When
trying to engage a lead today, the situation is even more acute as desk phones are vanishing and business emails are clogged. Ironically the clogging is mostly
due to marketing automation deployments mentioned before.
The good news is
that multiple new channels, when used correctly, provide better response rate
and higher quality engagement due to the professional context they contain.
In addition, engaging across multiple channels
offers a “warmer” introduction that starts the conversation in a more
consultative manner rather than the old cold and pushy manner of both email and phone.
Predictive analytics instead of linear formulas and playbooks
When it comes to forecasting the quarter or deciding on the next best action a salesperson needs to take with a prospect, we are seeing a shift from simple formulas or playbooks created by sales management to more sophisticated, self-learning predictive analytics solutions. The most popular use case for predictive analytics remains lead scoring followed by revenue forecast with the automated “playbook” trailing behind mostly due to the complexity of enterprise sales process and the lack of data available for such algorithms to be accurate.
Another common mistake impacting the reliability of predictive analytics is the that customer tend to forget the CRM data quality (1st item in this list) and implement sophisticated predictive algorithms based on stale and inaccurate data which obviously yields poor results.
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