Artificial Intelligence- Mending the gaps between sales and marketing


Closing the chasm between sales and marketing using Artificial Intelligence. Let’s face it: the lines between sales and marketing are blurring. However, the irony is, many companies are still facing challenges in mending the gaps between their sales and marketing functions. Unfortunately, this gap exists during a time when effective collaboration has never been more important. Often a result of the two departments’ differing priorities: marketing professionals focus on creating educative, solution-driven content formats, while the sales people lay emphasis on product spiel highlighting the features and reviews. From both sales and marketing standpoint, here are the common [information] gaps that need to be addressed:

• Prospecting and customer intelligence
• Lead scoring and qualification
• Automated relevant recommendations and offers
• Building brand advocacy through lead nurturing

Top performing companies believe they could be doing a better job of using customer data to align marketing and sales with the overarching business goals — which is not an easy task.

And so, integrating the two functions [sales and marketing] in order to measure the efficacy of both needs a common framework: which is AI.
Collaboration in its most fundamental version is behaviour, and AI supports the cohesion of sales and marketing by providing AI marketing platform that tests, tracks, and analyzes information from across channels while simultaneously making that information accessible to every department. Moreover, AI integrates seamlessly into existing tech stacks, including a sales team’s CRM, immediately transforming it into a viable marketing tool. However, selecting the right business application platform that is most suitable for your company is based on your prioritization of short-term and long-term challenges.

Using Data intelligently
The Indian business environment has become a breeding ground for AI start-ups. Industries that thrive on proliferation of massive data such as Healthcare, Airlines and Oil refineries among a host of industry domains engage AI tools to convert data into information and scale that up to knowledge and eventually create cognitive systems. Such is the attention on AI driven systems that research firm IDC suggest that more than 50% applications built will be cognitive in nature by 2018.

Further, massive investments are made by retailers and technology industry players on AI tools to optimize business functions automate supply chain scenarios. Two distinct use cases where investments are made on AI while creating value in the ecosystem include:

• Marketing and Segmentation – Datasets are used by AI models to predict and prioritize successful campaigns and channels that provide insights for efficient decision making
• Supply chain optimization – AI tools can also predict future demand supply, offer sales solutions that enhance productivity levels of the salesforce and optimize/automate inventory management.

AI ensures that sales teams more effectively utilize leads generated from marketing by pairing unique customer preferences with learned consumer behaviours to maximize both the reach of a marketing campaign and the likelihood of a conversion.
That said, here are 5 ways in which AI can bridge the gap between sales and marketing.

Intelligent lead nurturing
To run intelligent nurturing campaigns, companies need smart tools that marketing teams can deeply analyze lead data such as their browsing activities, profile information, purchasing behaviour, and service requests, among others. That way, marketers can automatically assign the lead to the most relevant lead nurturing campaign and share the valuable customer insights with sales. This approach is highly personalized and allows both sales and marketing reps to run multiple advanced omnichannel campaigns concurrently.

Determining content strategy and attribution
Customer journey is content journey for sales and marketing reps to work in greater synergy. By understanding the full cycle sales funnel allows for marketers to create high quality content that attracts and keeps customers engaged. Creating bespoke content based on each buyer stage – be it awareness, consideration, purchase or advocacy help marketers to increase investments in the right sources that deliver the best results for sales generating not just lead volume but also revenue. A strong bond of marketing and sales together with the use of AI-powered tools can significantly improve marketing and sales workforce’s performance.

Lead scoring
The AI algorithms help marketers in mining the accumulated data to score leads and determine their readiness to be handed off to sales to deliver high-quality leads. Applying cutting-edge technology like data analysis and machine learning will automatically calculate a score based on the likelihood that the lead will convert or contribute to a large revenue impact. As a result, intelligent tools transform marketing and sales departments bringing their collaboration to the next level of efficacy. That way, intelligent lead scoring augments the marketing and sales activities, making them more measurable, consistent, efficient and controllable.

Smart lead routing
AI powers human intelligence by defining which sales rep receives the lead based on their expertise, pipeline load or other configurable parameters. Applying AI-powered tools also allows for allocating a lead to the most suitable sales rep based on the lead’s preferences, background, and history of interactions results in a higher percentage of sales conversion. This enables organizations map business outcomes by taking into account a sales team’s workload to align pipeline load and performance.

Conclusion

Several real time start-up examples drives home the fact that there’s significant amount of enthusiasm around AI applications and experts believe that all web interfaces will soon get replaced by AI in the recent future. This will have huge implication for the sales and marketing reps, as they will be increasingly using sales automation platforms to streamline how leads are processed and closed.
However, AI start-ups in India are at nascent stages unlike elsewhere in the world. Moreover, there is tremendous scope for AI, especially in the retail industry, that remains to be tapped. And although AI adoption raises pertinent socio-economic challenges, but it appears to be the most exciting and widespread potential capital efficiency and ROI opportunity witnessed over few years in the past.

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