New mobile apps to keep an eye on
Hendrerit enim egestas hac eu aliquam mauris at viverra id mi eget faucibus sagittis, volutpat placerat viverra ut metus velit, velegestas pretium sollicitudin rhoncus ullamcorper ullamcorper venenatis sed vestibulum eu quam pellentesque aliquet tellus integer curabitur pharetra integer et ipsum nunc et facilisis etiam vulputate blandit ultrices est lectus eget urna, non sed lacus tortor etamet sed sagittis id porttitor parturient posuere.
- Lorem ipsum dolor sit amet consectetur rhoncus ullamcorper ullamcorper
- Mauris aliquet faucibus iaculis dui vitae ullamco
- Posuere enim mi pharetra neque proin vulputate blandit ultrices
- Posuere enim mi pharetra neque  pellentesque aliquet tellus proindi
What new social media mobile apps are available in 2023?
Sollicitudin rhoncus ullamcorper ullamcorper venenatis sed vestibulum eu quam pellentesque aliquet tellus integer curabitur pharetra integer et ipsum nunc et facilisis etiam vulputate blandit ultrices est lectus vulputate eget urna, non sed lacus tortor etamet sed sagittis id porttitor parturient posuere.
Use new social media apps as marketing funnels
Eget lorem dolor sed viverra ipsum nunc aliquet bibendum felis donec et odio pellentesque diam volutpat commodo sed egestas aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod eu tincidunt tortor aliquam nulla facilisi aenean sed adipiscing diam donec adipiscing ut lectus arcu bibendum at varius vel pharetra nibh venenatis cras sed felis eget.
- Lorem ipsum dolor sit amet consectetur fringilla ut morbi tincidunt.
- Mauris aliquet faucibus iaculis dui vitae ullamco neque proin vulputate interdum.
- Posuere enim mi pharetra neque proin  bibendum felis donec et odio.
- Posuere enim mi pharetra neque proin aliquam mauris at viverra id mi eget.
“Eget lorem dolor sed viverra ipsum nunc aliquet bibendum felis donec et odio pellentesque diam volutpat.”
Try out Twitter Spaces or Clubhouse on iPhone
Nisi quis eleifend quam adipiscing vitae aliquet bibendum enim facilisis gravida neque velit euismod in pellentesque massa placerat volutpat lacus laoreet non curabitur gravida odio aenean sed adipiscing diam donec adipiscing tristique risus amet est placerat in egestas erat imperdiet sed euismod nisi.
What app are you currently experimenting on?
Eget lorem dolor sed viverra ipsum nunc aliquet bibendum felis donec et odio pellentesque diam volutpat commodo sed egestas aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod eu tincidunt tortor aliquam nulla facilisi aenean sed adipiscing diam donec adipiscing ut lectus arcu bibendum at varius vel pharetra nibh venenatis cras sed felis eget.
The cloud computing revolution has brought a massive data tsunami.
Suddenly, companies were drowning in more data than they knew what to do with.Â
Data analysts became the lifeguards, using data visualization to make sense of "what happened" and "why."
But that was just the warm-up act.
When Machine Learning Started Shaking Things Up
Remember when machine learning started going mainstream?
It started being used in all sorts of consumer applications:
- Content recommendations (Netflix, Spotify, Amazon)Â
- Fraud detection (Paypal, Stripe)
- Financial forecasting (Intuit/Mint)
And while it wasn’t nearly the type of AI earthquake that came after more recent innovations like OpenAI/ChatGPT, it sure improved our lives!
Only one hiccup –Â
For companies that weren’t tech giants, implementing machine learning took serious effort and resources.
Not every business could just flip the switch.
No-Code AI for the Non-TechnicalÂ
With time, automated machine learning (AutoML) solutions have emerged.
No-code interfaces allowed non-technical domain experts to automate various processes like data analysis, model building, model retraining, and even employee enablement.
And even though these solutions offered a quick way for go-to-market teams to spin up models and tackle everyday challenges – gaps still remained.
- Data hygiene - Teams still struggled to prepare their (messy) data for analysis
- Next best action - Teams didn’t always know what they should do next with their predictions
That's where LLMs came into the picture, giving us unprecedented ability to:
- Clean data and remove errors before analyzing it
- Gain visibility into what our next best course of action should be
Now Predictive AI 2.0 is Changing Everything
When you take a predictive model and combine it with proprietary LLMs trained on the company’s data, you end up with what we call Predictive AI 2.0
Large language models (LLMs) are advanced AI models that can read and understand data almost like humans.Â
But LLMs are not exclusive to content writing. With LLMs trained on B2B data, Predictive AI can:
- Fill in any holes or missing pieces in your B2B data
- Weed out errors and inconsistencies
- Surface anomalies and simplify reporting
- Improve model output readability
- Fine tune predictive modelsÂ
- Let you ask your model questions
Pretty incredible stuff, right?
How the Savviest B2B Leaders Are Winning With It
The smartest B2B revenue leaders are tapping into Predictive AI to gain a huge competitive edge:
1. Spot revenue opportunities in existing pipelines
2. Optimize sales strategies based on forward looking trends
3. Detect churn trends and product-usage changes
By leveraging Predictive AI 2.0, these leaders can steer their ships with data-driven decisions that actually move the revenue needle.
The Future Is Happening Right Now
While others are still reacting to the past, the savviest leaders are using Predictive AI to create the future they want.
If cutting-edge technologies like this are being embraced, can you afford not to take a look?
Food for thought as you chart your own course.
