It’s still early days for sales automation. But by experimenting now with use cases based on real business needs, companies can already get ahead of their competitors.
Marketing automation has been around for more than 20 years, and today many organisations reap the benefits of automated emails, segmentation, account-based marketing, and more.
While sales automation is built on the same core of customer data as marketing automation solutions are, the principles and degrees of automation in sales are still largely undefined.
We need to start by aligning the systems and processes between sales & marketing, as this then enables the functions to work seamlessly towards the same goals. Sales & marketing managers and leaders need to understand how to find the critical use cases for the business, and the potential and limitations of new digital tools.
The current state of sales automation
Currently, sales-automation processes can best be described as point-to-point solutions: i.e. self-service digital sales platforms ranging from traditional B2C ecommerce to selling complex configurable solutions in the B2B environment. There are also the much hyped — but not necessarily validated — new tools like chatbots and call automation.
Most of the current sales automation solutions available today automate only a small part of the customer journey, such as identifying new sales opportunities, sending emails, automating tasks, performing sales call analysis, analysing next best actions and creating proposals. But it is still difficult to see how these tools integrate into sales processes and the architecture as a whole.
What can and should be automated
At the end of the day, you can’t automate everything in sales. This is because sales is a trust-game and the bigger the sell, the bigger the need for face-to-face interactions to instil that mutual trust.
Thinking of the different sales use cases to automate, it is good to start with business specific goals and opportunities, as well as bottlenecks in the customer journey. On a more general level, the main goals of sales automation fall into two categories:
- Increasing sales efficiency (i.e. automating repetitive work done by salespeople)
- Increasing sales effectiveness (i.e. automating value-creating interactions)
You need to serve both goals in order to succeed in holistic sales automation.
The reality is that nearly two thirds of a salesperson’s time goes into non-revenue-generating tasks (Time Management Sales Study 2017).
Most salespeople say their favourite deal-related activity is relationship building (Day in the life of Salesperson, Salesforce).
Not everything can be automated, and nor should it be. From a technical point of view, you could automate a lot. But from a return on investment perspective, automations that eliminate minor tasks performed once every month for 10 minutes is a wasted effort.
The more complex problem is to understand from the customer experience point of view which automations are effective. All automations aiming to increase effectiveness should be subject to proof of value for the customer before starting the project.
Enabling sales efficiency with automation
Customer data is the catch-22 problem of sales automation: If you don’t have data, you cannot build efficient sales pipelines, and if you don’t have efficient sales pipelines, you don’t have the data or time to enable effective sales. The same salespeople who would want to use customer data are usually responsible for maintaining and enriching that data. So the whole subject of data collection and management creates a huge issue with time to value.
The starting point for sales automation is having a flexible enough customer relationship management (CRM) tool or customer data platform (CDP) in place, complemented with tools that automatically enrich that database from 1st or 3rd party systems. The key to enabling efficiency is getting relevant customer data — such as contact details, transactional and behavioural data — into a format that can be readily utilised by sales automation tools.
Increasing sales effectiveness
The next important question is how to further develop the effectiveness of sales through automation. At this stage, we can start to think of how to automate the management of the customer base — including segmentation — and how to enable automations in the different phases of the customer journey.
It’s worth looking at advanced dash-boarding and AI-enabled suggestions of next best actions at a customer-level. Through experimentation, you can find new ways to make relevant segmentations and combine those with concrete sales activities. At this stage, buyer personas will not be based on demographics, but rather on complex customer cohorts that describe similar customers in terms of multiple attributes.
Sales automation needs experimentation
But there is still a gap: how to align new capabilities and insights from sales automation systems with concrete sales activities, either in digital or offline. Effectiveness is enabled by automating core revenue-generating sales activities, but all new use cases need to be validated in their early stages by manual experimentation.
Hence, the next level issue is how to scale the implementation of new automation use cases even further. Having systems that, in theory, enable effective sales automation, is not enough. They need to be complemented by proactive ideation and experimentation from people with a business- and customer-driven mindset. Furthermore, when the needs of customers change, we need to revise core automations to reflect these new needs.
The roadmap to sales automation need not be difficult. It is not mandatory — indeed, not even recommended — to start building a perfect platform before knowing possible use cases. However, it may be necessary to tackle and re-define any not-so-lean sales systems and processes. Some companies may need to start right at the beginning by introducing modern technological capabilities.
Sales automation is still in its early stages. Being a first mover always comes with risk, but learning from first movers in marketing automation, the best way forward seems to be to build the business case first, then experiment lightly and automate one core use case at a time.
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Originally published at https://www.columbiaroad.com.