What do you do if your social selling strategy needs a personal touch from machine learning algorithms?

The whole point of social selling is that it's about being human. We know that personality beats brand everyday as Linkedin data shows that human posts get 561% more engagement than brand posts. This is why we have created our own social selling methodology. It's a methodology certified by the Institute of Sales Professionals. It means that a business knows they are getting a modern set of processes that when they use them, there is no luck required. You do this, you do this, you get this result.

Use machine learning algorithms to analyze the collected data and identify patterns or trends that can help personalize your social selling approach. This can involve clustering customers into segments, predicting their future behavior, or recommending personalized products or content

Social selling is a strategy that relies on building personal relationships with potential customers through platforms like social media. Machine learning algorithms can help enhance this strategy by providing personalized recommendations, predicting customer behavior, and automating processes to make interactions more efficient and effective. Here are some basics of using machine learning in your social selling strategy: Data collection and analysis: Machine learning algorithms can analyze large amounts of data from social media platforms to identify patterns and trends in customer behavior. This data can then be used to personalize interactions and tailor messaging to individual preferences.

Il s'agit simplement de comprendre comment les ordinateurs peuvent apprendre à partir de données pour prendre des décisions. Imaginez-le comme un assistant intelligent qui apprend de ses expériences.

Data collection is a crucial aspect of implementing machine learning algorithms in your social selling strategy to enhance its personal touch. Here are some key points to consider when collecting data for this purpose: Customer data: Collect relevant customer data from various sources such as social media profiles, interactions, preferences, and purchase history. This data should be rich and diverse to provide a comprehensive understanding of each customer. Social media analytics: Utilize social media analytics tools to gather insights on customer engagement, interactions, feedback, and sentiment. These analytics can help identify trends and patterns that can be used to personalize customer interactions.

La première étape consiste à collecter des données pertinentes sur vos clients, vos prospects et vos interactions sur les réseaux sociaux. Plus vos données sont riches et diversifiées, plus votre modèle d'apprentissage automatique sera précis.

Gather relevant data about your customers, their preferences, and their behavior on social media platforms. This can include demographic information, past purchase history, and engagement with your brand.

Pensez aux informations que vous pourriez utiliser pour améliorer vos interactions avec les clients. Il peut s'agir de données sur leurs préférences, leurs comportements en ligne, ou même leurs besoins spécifiques.

The only data you need to collect is the data you are creating as a social seller. This is the data before a lead hits the CRM. This is why we have created our own social selling methodology. It's a methodology certified by the Institute of Sales Professionals. It means that a business knows they are getting a modern set of processes that when they use them, there is no luck required. You do this, you do this, you get this result.

Choosing the right machine learning algorithm is crucial for implementing a personalized touch in your social selling strategy. Here are some key considerations to help you decide on the best algorithm for your needs: Collaborative Filtering: Collaborative filtering algorithms, such as user-based or item-based recommendation systems, can be effective for providing personalized product recommendations based on customer behavior and preferences. These algorithms analyze past interactions and similarities between customers or products to make relevant suggestions. Content-based Filtering: Content-based filtering algorithms analyze customer preferences and historical data to recommend products or content that match their interests.

Chasing any algorithm is a road to nowhere. This is why we have created our own social selling methodology. It's a methodology certified by the Institute of Sales Professionals. It means that a business knows they are getting a modern set of processes that when they use them, there is no luck required. You do this, you do this, you get this result.

To do social selling properly you do need any tools or salestech other than LinkedIn. So many "snake oil" sellers will try and convince you that you need tools to to chase an algorithm. This is why we have created our own social selling methodology. It's a methodology certified by the Institute of Sales Professionals. It means that a business knows they are getting a modern set of processes that when they use them, there is no luck required. You do this, you do this, you get this result.

Mettez en place un moteur de personnalisation qui utilise l'apprentissage automatique pour analyser les données des clients et des prospects, puis recommander des actions personnalisées en fonction de leurs préférences et de leurs comportements passés.

Implementing a personalization engine powered by machine learning algorithms can greatly enhance your social selling strategy by providing a tailored and engaging experience for your customers. Here are some key components and considerations to keep in mind when building a personalization engine for your social selling strategy: Data Collection and Integration: Gather and consolidate customer data from various sources, including social media interactions, website behavior, CRM systems, and surveys. Ensure that the data is clean, relevant, and up-to-date to provide accurate insights for personalization. Customer Segmentation: Utilize machine learning algorithms to segment customers based on attributes such as demographics, behavior, etc.

Implement the insights gained from the data analysis to personalize your interactions with customers on social media. This can include targeted advertising, personalized recommendations, or tailored messaging based on individual preferences.

Une fois que vous avez compris comment fonctionne l'algorithme, utilisez-le pour rendre vos interactions avec les clients plus personnalisées. Cela signifie simplement ajuster votre approche pour mieux répondre aux besoins de chaque client.

Continuous learning is essential for improving the effectiveness of machine learning algorithms in your social selling strategy and ensuring that personalized touch remains relevant and up-to-date. Here are some key aspects to consider when implementing continuous learning in your social selling strategy: Real-Time Data Updates: Make sure that your machine learning algorithms have access to real-time data updates from various sources, including social media platforms, CRM systems, and customer interactions. Continuous updates will ensure that the algorithms base their recommendations on the most recent customer behavior and preferences. Feedback Loop: Establish a feedback loop that collects customer feedback, reviews, and interactions.

Implement mechanisms for your ML models to learn continuously from new data. As your system interacts with customers, it should adapt and improve its recommendations over time. This requires setting up a feedback loop where the model's outputs are continually used to refine and update the model itself.

Assurez-vous que vos pratiques respectent la vie privée de vos clients et sont éthiques. Il s'agit simplement de faire ce qui est juste et respectueux.

Address ethical considerations related to the use of machine learning in social selling. This includes ensuring customer data privacy, securing informed consent for data usage, and being transparent about how ML algorithms influence the content and recommendations shown to users.

So, the question is "how can you increase your personal touch with machine learning"?!? Perhaps the answer is to try and approach this from the other side. What is the desired outcome? If what are looking for is a deep relationship with someone who WANTS to work with you it is best to treat that with the reverence it deserves. Social media platforms are brilliant for keeping people engaged at scale in a way that email/advertising etc cannot, but they are not good for spamming so be very careful with what you try to automate otherwise you run the risk of annoying people through yet another channel!

N'oubliez pas de partager vos expériences avec d'autres. Cela peut être une excellente occasion d'apprendre les uns des autres et de progresser ensemble dans le domaine du social selling.

En plus des aspects techniques, prenez en compte d'autres facteurs tels que l'impact sur l'expérience client, la formation du personnel et la mesure de la performance. Assurez-vous également de surveiller de près les résultats et d'ajuster votre stratégie en conséquence.