With a career spanning 15 years in wireless telecommunications, Jason Raymer is currently Senior Vice President of Revenue for iQmetrix, North America’s only provider of Interconnected Commerce solutions designed to power the telecom retail industry.
Jason spent the early days of his career navigating the intricacies of consumer electronics retail, which quickly evolved into revenue and operations roles at Tier 1 and 2 North American telecom carriers. These experiences have proven invaluable to his current leadership role, which has a focus on a strategic vision that propels organisations to new heights.
Jason’s expertise lies in crafting revenue-generating strategies, leveraging technology to help telecom retailers optimise their operations, and fostering strong client relationships. As a seasoned industry expert, his commitment is driving iQmetrix’s success in an ever-changing landscape, and propelling the company to the forefront of the telecom industry.
He is a champion of initiatives that harness the power of emerging technologies, ensuring iQmetrix solutions remain agile and competitive in a rapidly evolving digital ecosystem.
In an exclusive interview with the International Finance Magazine, Jason Raymer, Senior Vice President of Revenue at iQmetrix, offers a comprehensive insight into the transformative impact of AI within the telecom retail sector, delving into topics such as AI-driven chatbots and the nuanced utilisation of AI analytics in examining customer behaviour, and much more.
Q) How have telecom retail companies found success by implementing AI into their business models, and what specific areas have shown improvement?
A) Most AI solutions in use today are currently serving the organisation’s operations where they are experiencing an increase in productivity. This can be seen today with examples such as the use of AI-based workforce scheduling software that can easily use sales data to predict the volume of traffic, the number of employees needed to support that traffic, and which staff member should have each shift that will perform best, based on their historical sales. AI can also help immeasurably in improving the store rep’s knowledge and, ultimately, the customer experience. Without AI, the associate in a store must juggle knowledge of all kinds of rate plans, the promotions available, different device options, and much more. Besides — all of them have vast amounts of documentation that needs to be explained to the customer. This can be dramatically eased by AI tools ingesting all the necessary information and turning it into a search model, whereby the rep asks what the customer wants and inputs it, and the AI tool offers the optimal package for that customer.
Q) How does AI make the retail customer experience better?
A) Beyond efficiency and productivity gains, AI can unlock a vastly improved customer experience in many ways. One is by offering personalised shopping based on the customer’s wants and needs, no matter where they encounter the brand—online, in store, or on social media for example. Or it could be by improving the quality of the user’s experience with customer service, with AI tools triaging issues and feeding instant solutions to the agent, or even directly to the consumer. This improved retail experience in turn can radically improve customer loyalty and retention. The business’ bottom line can be boosted from both angles—both lower overhead costs and reduced customer attrition.
Q) How might the expanding influence of AI in the telecom retail sector impact job security for human workers?
A) Unlike most industries that are starting to leverage AI, the impact on job security in the near term could be those that hold administrative positions within telecom retail companies, where AI can take over administrative tasks. With that said, there may be a crossroads when AI is used to solve the individual needs of a consumer to the point that a retail sales associate is adding less value than the technology. Shifting the soft skills of sales into a hard skill reduces the need for training and maintaining a soft-skilled workforce to realise revenue targets. At this point, the role of the retail sales associate could dramatically morph or even be phased out.
Q) How can AI play a role in accelerating the advancement of innovative technologies related to 5G, particularly in countries such as Canada, where 5G accessibility is currently lagging?
A) The lagging advancement in 5G accessibility can be partly due to the ageing tech stacks that the Tier 1 Carriers are supporting. This tech debt has created internal focuses that prioritise transformation, vendor consolidation, and take-away resources that could be focused on the commercialisation of 5G. AI should play a pivotal role in reducing the bulk of these aged tech stacks and enable investment in next-generation networks.
Q) Since there has been increasing use of AI analytics to analyse customer behaviour, what impact it will have on shaping retail strategies?
A) The use of AI analytics to analyse customer behaviour will allow retailers to more accurately action their strategies. Specifically, around the effectiveness of personalised marketing offers. Personalised offers are designed to promote the right product, to the right customer and the right time for conversion. With the use of AI analytics, the shift and volatility in customer behaviour become predictable and can be actioned accordingly. No longer will retailers be working from 36 months of historical seasonality. Instead, real-time/near-time data will be used to react to market conditions in a way we have never seen.
Q) How do you view the effectiveness and potential impact of AI-powered chatbots for online customer support and in-store virtual assistants?
A) The effectiveness of these tools is predicated on the data set that is available to them. The actual customer experience and engagement with these tools will be based on the amount of personalised data each retailer has of their consumers. A general use of chatbots for questions and conventional support will meet the needs of most consumers as they have been in existence for many years. However, to implement this with a consumer who expects an Amazon-like experience will require AI to have and use the personal information that all companies are trying to keep secure.
Q) What key considerations must telecom retailers address regarding privacy and bias when implementing AI, and how have these concerns led to calls for regulatory changes and legislation around AI?
A) Regulation around AI is needed, and with countries such as Canada leading the way in proposing laws to support this, many more will follow. With telecommunications being federally regulated today, the legislation that will come is guaranteed to alter how AI can and will be used.
Q) How does better customer experience, facilitated by AI, play a crucial role as a cornerstone of customer retention?
A) At this time, it is retailers who are predicting what the customer experience needs to look like in order to drive customer retention. As AI gets to scale and becomes consumer-facing, the feedback and engagement of those customers will give retailers the best understanding. The next generation that is coming to the marketplace, Gen Alpha, has grown up with a device in their hands and is not afraid of digital. We would expect that retailers striving to capture this generation should be able to do this with ease with the right implementation of AI. Whereas older generations still have trust issues with certain technologies and expect a hybrid experience when available. For those telecom retailers that need to address multiple generations, the retention of these customers will not be solely on AI, but will certainly assist in acquiring and retaining Gen Alpha consumers in the marketplace.
Q) What kind of challenges does the implementation of AI have on large and small telecom retail businesses in 2024?
A) The greatest challenge is knowing where and who to invest with when trying to realise the benefits of AI. If companies try to sit back and wait for clear understanding, they are laggards by default. Organisations that have chosen to be early adopters have a learning curve and sunk cost associated with the bleeding-edge technology. The reality for most is that there needs to be clear AI use cases formed by the organisations that are putting AI into action today.