machine learning
Six Ways AI Can Become a Sales Management Enhancer
As a VP of Sales, I would spend the first 60-90 minutes of every day reviewing a dozen different news sources, looking for information about new technology, competitor announcements, proposed legislation, and M&A news. I looked for anything relevant in critical industries, news about our customers or their top customers, and staffing changes within their companies.
My goal was to identify anything that could negatively impact deals in play, threaten the customer base, disrupt the run rate business, as well as seize opportunities to break into a new company or displace a competitor. You feed your findings and speculations back to your team, along with suggestions, talking points, or specific directions to help them maintain or increase their success for the current quarter plus the next few quarters.
You look for trends and leading indicators that could help your team and your organization achieve greater success. Winning feels good, and the rewards make you want to achieve even more.
Artificial Intelligence (AI) will be able to do all this and more. It will be more consistent, analyze information without bias, and do so on a more timely basis.
1. Automated Intelligence Gathering
Focus your efforts where they add the most value. AI can automate the collection and analysis of data from multiple sources, including news feeds, legal updates, social media, and competitor websites. This automation can save considerable time and minimize the chances of missing relevant information. Natural Language Processing (NLP) can identify, categorize, and correlate relevant information, providing actionable insights without the need for manual review.
2. Enhanced Lead and Opportunity Identification
In addition to the correlations above, Machine Learning (ML) models can analyze trends and patterns in data to identify potential leads or opportunities for expansion. By understanding market movements, customer behaviors, historical behavior, and presumptive competitor strategies, AI can suggest new targets for sales efforts and highlight areas where teams could gain a competitive edge.
3. Improved Internal Communication and Collaboration
Sales is not just about selling but also about collaborating internally to create the best possible products and services to sell and identify the best approaches to generate awareness and interest in your offerings.
AI systems can serve as a central hub for information that benefits various departments within a company. By integrating with CRM, marketing, support, and other internal systems, AI can distribute tailored information to different teams, ensuring that everyone has the best insights to perform their roles effectively and promoting a more cohesive and coordinated approach to achieving long-term business objectives.
4. Forecasting and Predictive Analytics
With the ability to process vast amounts of data, AI should significantly improve forecasting accuracy. Predictive analytics can estimate future sales trends, customer demands, and market dynamics, providing businesses with more opportunities to make better-informed decisions—better resource allocation, optimized sales strategies, and, ultimately, higher revenue will be the result.
5. Increased Efficiency and ROI
By automating routine tasks and providing deep insights, AI can free up sales and management teams to focus on strategic activities. The efficiency gains from AI can result in significant cost savings and a higher return on investment (ROI) as teams do more with less, capitalize on opportunities faster and more effectively, and ultimately make more money for themselves and their company.
6. Continuous Learning and Improvement
Machine learning models will improve over time as they process more data, meaning the insights and recommendations provided by AI will become increasingly accurate and valuable, helping businesses continuously refine their strategies and operations for better outcomes.
Future Perspectives
While it’s true that AI may not yet be ready to take over complex roles like enterprise sales, its potential to enhance these roles is undeniable. As AI technology continues to evolve, its ability to provide highly accurate forecasts, improve win rates, shorten sales cycles, and enhance competitiveness will only grow. The future of AI in sales and business management is not just about automation but about augmenting human capabilities to create more effective, efficient, and thriving organizations that are better able to compete in an increasingly competitive global landscape.
So, what do you think? Will this work? Will it be good enough if everyone is doing it? Leave a comment and let us know.
Using Themes for Enhanced Problem Solving
Thematic Analysis is a powerful qualitative approach used by many consultants. It involves identifying patterns and themes to better understand how and why something happened, which provides the context for other quantitative analyses. It can also be utilized when developing strategies and tactics due to its “cause and effect” nature.
Typical analysis tends to be event-based. Something happened that was unexpected. Some type of triggering or compelling event is sought to either stop something from happening or to make something happen. With enough of the right data, you may be able to identify patterns, which can help predict what will happen next based on past events. This data-based understanding may be simplistic or incomplete, but often it is sufficient.
But people are creatures of habit. If you can identify and understand those habits and place them within the context of a specific environment that includes interactions with others, you may be able to identify patterns within the patterns. Those themes can be much better indicators of what may or may not happen than the data itself. They become better predictors of things to come and can help identify more effective strategies and tactics to achieve your goals.
This approach requires that a person view an event (desired or historical) from various perspectives to help understand:
- Things that are accidental but predictable because of human nature.
- Things that are predictable based on other events and interactions.
- Things that are the logical consequence of a series of events and outcomes.
Aside from the practical implications of this approach, I find it fascinating relative to AI and Predictive Analysis.
For example, you can monitor data and activities proactively by understanding the recurring themes and triggers. That is actionable intelligence that can be automated and incorporated into a larger system. Machine Learning and Deep Learning can analyze tremendous volumes of data from various sources in real-time.
Combine that with Semantic Analysis, which is challenging due to the complexity of taxonomies and ontologies. Now, that system more accurately understands what is happening to make accurate predictions. Add in spatial and temporal data such as IoT, metadata from photographs, etc., and you should be able to view something as though you were very high up – providing the ability to “see” what is on the path ahead. It is obviously not that simple, but it is exciting.
From a practical perspective, keeping these thoughts in mind will help you see details others have missed. That makes for better analysis, better strategies, and better execution.
Who wouldn’t want that?
Biometric Identity Theft
Recently, I have been researching the potential for fraud and identity theft using fingerprints from photos posted on social media. Last week Amazon released its “Amazon One” Palm Scanner to pay for purchases when shopping. That announcement made me wonder, what are the potential implications for fraud and identity theft using biometric data taken from images?
Technology continues to improve rapidly, which often means, “Where there is the will, there’s a way.”
There are a surprising number of ways to accurately identify someone from a photo or video. Moreover, there is technology to copy fingerprints from social media photos taken up to three meters away. New technology has been proven effective at using 3D printing technology to create “fake fingerprints” that bypass many fingerprint scanners.
Since fingerprints can be copied from photos taken up to three meters away, can a palm print be copied from a photo taken 5-10 meters away? That question led to an interesting but unscientific experiment where I took pictures of my hand, enlarged them, measured the distance between the ridges and furrows of my fingers and my palm, and compared the results. Spoiler – probably not (yet).
There are several areas where that distance was similar for both my fingers and palm. But, there were also areas on my palm where the average distance between “landmarks” was 3-5+ times greater. It turns out that a palm image is often segmented into 3-4 distinct regions for identification purposes, likely due to this type of variation. This link was helpful in understanding the process.
This research led to an idea for a chip-based embedded filter for smart devices and laptops. It would obfuscate key biometric information when extracting the data for display without affecting the integrity of the original stored image. This functionality would automatically provide an additional layer of privacy and data protection. It would require optimized object detection capabilities (possibly R-CNN) that were highly efficient and run on a capable but low-energy processor like the Arm Cortex-M. Retraining and upgrades would be accomplished with firmware updates.
Edit 2020-10-13: This article on “Tiny ML” from Medium.com is the perfect tie-in to the abovementioned idea.
While Amazon’s technology is much newer and presumably at least partially based on their 2019 Patent Application (which does look impressive), it makes you wonder how susceptible these devices might be to fraud given reports of the scans occurring “almost instantaneously.” Speed is one aspect of successful large-scale commercial adoption, but the accuracy and integrity of the system are far more important from my perspective.
Time will tell how robust and foolproof Amazon’s new technology really is. Given their reach, this could occur sooner rather than later. Ultimately, multiple forms of biometric scans (such as a full handprint with shape, palm, and fingerprints or a retina scan 2-3 minutes prior to the palm scan to maintain performance) may be required for enhanced security, especially with mobile devices.
Additional Resources:
- In-depth NIH document that is helpful in understanding the process and issues.
- A Simplified Guide to Fingerprint Analysis
- This is a 2012 Fingerprint and Palmprint Recognition Approach Report from the European Journal of Scientific Research that has many images and very good explanations.
- “A Textural Approach to Palmprint Identification“
New Perspectives on Business Ecosystems
One of the many changes resulting from the COVID-19 pandemic has been a sea change in thoughts and goals around Supply Chain Management (SCM). Existing SCM systems were up-ended in mere months as it has become challenging to procure raw materials to components, manufacturing has shifted to meet new unanticipated needs, and logistics challenges have arisen from health-related staffing issues, safe working distances, and limited shipping options and availability. In short, things are a mess!
Foundational business changes will require modern approaches to Change Management. Change is not easy – especially at scale, so having ongoing support from the top down and providing incentives to motivate the right behaviors, actions, and outcomes will be especially critical to the success of those initiatives. And remember, “What gets measured gets managed,” so focusing on the aspects of business and change that matter will become a greater focus.
Business Intelligence systems will be especially important for Descriptive Analysis. Machine Learning will likely play a larger role as organizations seek a more comprehensive understanding of patterns and work toward accurate Predictive Analysis. And, of course, Artificial Intelligence / Deep Learning / Neural Networks should accelerate as the need for Prescriptive Analysis grows. Technology will provide many of the insights needed for business leaders to make the best decisions in the shortest amount of time, which is both possible and prudent.
This is also the right time to consider upgrading to a collaborative, agile business ecosystem that can quickly and cost-effectively expand and adapt to whatever comes next. Click on this link to see more of the benefits of this type of model.
Whether you like it or not, change is coming. So, why not take a proactive posture to help ensure that this change is good and meets the objectives your company or organization needs.
Changes like this are all-encompassing, so it is helpful to begin with the mindset, “Win together, Lose together.” In general, it helps to have all areas of an organization moving in lockstep towards a common goal, but at a critical juncture like this, that is no longer an option.
Blockchain, Data Governance, and Smart Contracts in a Post-COVID-19 World
The last few months have been very disruptive to nearly everyone across the globe. There are business challenges galore, such as managing large remote workforces – many of whom are new to working remotely and managing risk while attempting to conduct “business as usual.” Unfortunately, most businesses’ systems, processes, and internal controls were not designed for this “new normal.”
While there have been many predictions around Blockchain for the past few years, it is still not widely adopted. We are beginning to see an uptick in adopting Supply Chain Management Systems for reasons that include traceability of items – especially food and drugs. However, large-scale adoption has been elusive to date.
I believe we will soon begin to see large shifts in mindset, investments, and effort towards modern digital technology driven by Data Governance and Risk Management. I also believe that this will lead to these technologies becoming easier to use via new platforms and integration tools, which will lead to faster adoption by SMBs and other non-enterprise organizations, and that will lead to the greater need for DevOps, Monitoring, and Automation solutions as a way to maintain control of a more agile environment.
Here are a few predictions:
- New wearable technology supporting Medical IoT will be developed to help provide an early warning system for disease and future pandemics. That will fuel a number of innovations in various industries, including Biotech and Pharma.
- Blockchain can provide data privacy, ownership, and provenance to ensure the data’s veracity.
- New legislation will be created to protect medical providers and other users of that data from being liable for missing information or trends that could have saved lives or avoided some other negative outcome.
- In the meantime, Hospitals, Insurance Providers, and others will do everything possible to mitigate the risk of using Medical IoT data, which could include Smart Contracts to ensure compliance (which assumes that a benefit is provided to the data providers).
- Platforms may be created to offer individuals control over their own data, how it is used and by whom, ownership of that data, and payment for the use of that data. This is something I wrote about in 2013.
- Data Governance will be taken more seriously by every business. Today companies talk about Data Privacy, Data Security, or Data Consistency, but few have a strategic end-to-end systematic approach to managing and protecting their data and company.
- Comprehensive Data Governance will become a driving and gating force as organizations modernize and grow. Even before the pandemic, there were growing needs due to new data privacy laws and concerns around areas such as the data used for Machine Learning.
- In a business environment where more systems are distributed, there is an increased risk of data breaches and Cybercrime. That must be addressed as a foundational component of any new system or platform.
- One or two Data Integration Companies will emerge as undisputed industry leaders due to their capabilities around MDM, Data Provenance and Traceability, and Data Access (an area typically managed by application systems).
- New standardized APIs akin to HL7 FHIR will be created to support a variety of industries as well as interoperability between systems and industries. Frictionless integration of key systems become even more important than it is today.
- Anything that can be maintained and managed in a secure and flexible distributed digital environment will be implemented to allow companies to quickly pivot and adapt to new challenges and opportunities on a global scale.
- Smart Contracts and Digital Currency Payment Processing Systems will likely be core components of those systems.
- This will also foster the growth of next-generation Business Ecosystems and collaborations that will be more dynamic.
- Ongoing compliance monitoring, internal and external, will likely become a priority (“trust but verify”).
All in all, this is exciting from a business and technology perspective. Most companies must review and adjust their strategies and tactics to embrace these concepts and adapt to the coming New Normal.
The steps we take today will shape what we see and do in the coming decade so it is important to quickly get this right, knowing that whatever is implemented today will evolve and improve over time.
Where Ideas, Experiences, and Lessons Learned Intersect 