11th Jan, 2019
Denave, Team, Designation
Many pundits had predicted the demise of SFA with the rise of marketing automation platforms but SFA, thanks to fresh waves of innovation, continues to grow strong globally and is widely considered to have an optimistic future.
The latest figures released by Gartner Research, testifies to it. The report claims that the global Sales Force Automation (SFA) market grew 15.7% to $6.2 billion in 2017, and is expected to be world $9 billion by 2023.
In order to sustain the current adoption and market growth rate, SFA platforms need to evolve and also be future-ready in terms of technology implementation and functionality.
This fast-paced growth peppered by high market adoption requires SFA platforms to undergo sustained innovation trajectory with respect to the technology implementation and functionality. This would cater to the current market needs and shall also make SFA future-ready.
Making SFA Future-Ready
The burgeoning data flow is increasingly impacting customer touch-points and vice-versa. Undoubtedly, this is an elixir for the designing sales strategy. However, the truth is that in the process many relevant data get lost – not only due to the technology gap but also due to human errors.
Mapping purchase trends and understanding customer behavior are the premises around which current SFA platforms are already working. Stepping into the future, there would be increased uptake of AI and data analytics both prescriptive and predictive, catering to market disruptions – powering SFA in the desired direction.
We look at five major developments that shall herald immense change in the Sales Force Automation landscape paving way for it to be future-ready.
Speech Recognition and Localisation
One of the biggest hurdles in gathering high-quality data is incorrect data entry, chiefly stemming from a lack of writing proficiency. Data entry powered by speech-to-text will eliminate the loss of valuable last mile data at the point of entry.
Localization will help penetrate language barriers and reduce the chances of data entry error by enabling local teams to collect data in native languages, resulting in penetration beyond tier 1 cities. Speech recognition and localization will help improve the quality of data collected, impacting analytics and allowing stakeholders to make more informed decisions.
Artificial Intelligence and Machine Learning
Snehashish describes Artificial Intelligence and Machine Learning as ‘the biggest shot in the arm for sales’. And he has good reasons. Artificial Intelligence can augment an existing sales process by making it more ‘intelligent’, in terms of how it consumes and interprets data.
For example, by tracking past purchasing/buying trends and seasonality patterns, AI can determine the purchase propensity of a particular set of customers, making the sales process more targeted.
By leveraging machine learning for learning from past data, a Sales Force Automation platform can chart out the overall sales strategy, pricing strategy, in-store branding strategy, leading to more intelligent decision making and strategy execution.
Data Analytics and Predictive Analytics
Sales Force Automation platform enable the sales force to gather all market and point-of-sale data. By extending the data analytics framework within Sales Force Automation, decision-makers will get more detailed insights into the consumer market and enhance the decision-making process.
Predictive Analytics, on the other hand, powered by AI will give the sales force the insights and information they need to zero-in on the next new customer. By analyzing past purchasing patterns and purchase history, Predictive Analytics will allow stakeholders to take more informed strategic decisions.
For example, data analytics can help in analyzing customer demography, their feedback regarding products and competition, creating better pricing models and new sales & marketing campaigns that resonate with customers across the sales ecosystem.
By leveraging API integration within Sales Force Automation platforms, stakeholders will be able to integrate sophisticated third-party data entry and data management systems, reducing the human dependency on data collection thereby lowering the chances of error, leading to better analytics and enhanced decision making.
API integration will make the sales process more efficient by enhancing existing capabilities through the addition of custom, need-based systems.
Retail Audits powered by Image recognition and Deep Learning
Existing SFA platforms allow for thorough retail audits, but there is still some human element involved in verifying the stock. By integrating deep learning-based image recognition within SFA platforms, sales teams and stakeholders will be able to perform retail and stock audits without any human intervention, increasing the accuracy of the audits, allowing the stakeholders to take complete stock of the merchandise available for sales and for warehousing.
The manner in which buyers are interacting with products and the overall sales ecosystem is evolving, thanks in large part to the blurring of lines between digital and in-store experiences. As consumer experiences evolve and touch-points proliferate, stakeholders need to equip their customer-facing teams, i.e. the sales force with the right tools to ensure a successful interaction.
The technological developments expected to impact SFA will be implemented with this very goal in mind, making the platform future-ready.