There has been some talk lately about the Polestar 2 and whether it is a credible threat to the Tesla Model 3.

As such, I thought I would apply NLP to undertake sentiment analysis on social media feeds to see what real people were saying about it rather than just relying on automotive magazine reviews or the opinion of a few individuals.

But first, what is NLP and what is sentiment analysis?

NLP or Natural Language Processing can be said to be a subfield of linguistics, computer science and Artificial Intelligence (AI) concerned with the interactions between computers and human language. In particular, how to program computers to process and analyse large amounts of natural language data.

Sentiment analysis in simple terms is the use of NLP, text analysis, computational linguistics and biometrics to systematically identify, positive or negative sentiment in free text data.

Background to the analysis
The Polestar 2 is a new electric car developed by Polestar, a standalone electric offshoot jointly owned by Volvo and its Chinese parent company, Geely. As the flagship product, this is attempting to establish Polestar as a big player in the Electric Vehicle (EV) space and is seen as a direct competitor to the Tesla Model 3.

Key questions Vehicle Leasing companies might ask 
With the drive to electrification gathering pace, I assumed vehicle leasing companies might be keen to know what potential customers are saying about the Polestar 2 and what the implications might be for their business.

• Is consumer sentiment positive, negative or neutral overall?
• What do they like about it?
• What do they not like about it?
• Should we closely align ourselves with the manufacturer for discounts and captive finance deals?

 
In order to answer these questions, I performed sentiment analysis on a total of 25,000 English tweets related to “Polestar 2”.  These were classified as positive, negative or neutral sentiment. I then used word visualisation to bring this to life.

Technical stuff
I used a range of packages to perform this analysis in Python including Twint to extract tweets, WordCloud for word visualisation and NLTK to clean text (remove stopwords, tokenizing, lemmatizing).

And to classify tweets as positive, negative or neutral sentiment I used VADER (Valence Aware Dictionary and sEntiment Reasoner), which performs lexicon and rule based sentiment analysis and is specifically tuned to social media conversations.

Based on the actual words used in these tweets, a Compound Score is calculated and assigned to each tweet, ranging from -1 (most negative) to +1 (most positive).
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Why sentiment analysis is important, particularly on social media
Social media conversations provide a wealth of data to help brands understand who is out there, what they’re talking about and what's important to them. And best of all, these are real time conversations!

Of the 25,000 tweets, 48% were neutral. With these neutral tweets excluded, the remaining tweets are overwhelmingly positive (81%).

In addition, positive tweets are more positive than negative tweets are negative.

This has been visualised in the bubble plot below whereby the green bubble representing positive tweets is clearly larger and more to the right, whereas the red bubble representing negative tweets is clearly smaller and not as much to the left. 

NOTE: These Word clouds are based on actual tweets. The larger the words, the more frequently they appear in tweets.

Themes
The positive and negative themes distilled from the analysis are summarised below.

Part of the original question raised was whether Tesla should be concerned about the Polestar 2 being a credible threat to the Tesla Model 3. In short, the answer is yes.

​The positive themes from consumer conversations far outweigh the negatives and the comment ‘better Tesla’ is an interesting one.

​The negative themes are mainly around known technical issues and these will surely be resolved by the manufacturer sooner rather than later.

Value of the approach
From this, the value of sentiment analysis on social media conversations is clear. This can be accessed by just about any business to see what their customers or prospects are saying about their brand or products and that of their closest competitors too. Indeed, this approach could be used on a variety of conversational or text data across multiple social channels as well as review sites such as TrustPilot.

In essence, if you want to have direct access to consumer conversations about your brand or product and be able to monitor and react to consumer conversations in real time, then this is an area worth exploring. It gives you unfettered access to what is effectively your very own large scale focus group, available on-demand.

To harness the power of NLP and sentiment analysis for your brand, please email brendan@bluelabelconsulting.com to request your free Discovery consultation where we'll discuss your requirements and formulate a solution that is right for you.

While I hate the phrase ‘new normal’, it is clear that almost every business has been taking stock of where they are financially, operationally and competitively.
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But once you have an understanding of your situation and the broader market dynamics, what next?

Each business has reacted differently to changes in the market with many having limited or even stopped sales activity. And to determine how to turn the situation around you need to know what changes have occurred.

• Are there fundamental market dynamics that have changed?
• Is your product or service still required and if so, is it still required in the same quantity or form?
• Is the competition the same and are they prepared to spend?
• Are the customers you had the same customers who are coming back?

Identifying opportunities

As you look to go beyond the basics of opening up your sales and marketing operations, you should look to your Analytics teams for guidance.

​Ask them to provide the data and insight needed to expand activities profitably and to determine where new market and product opportunities are that perhaps weren’t around or strategically important before.

​Have new markets or segments opened up that you can serve with your current product offering and operational capabilities? What difficulties are the competition facing (or indeed threats to yourselves) that you can identify and react to? There may even be consolidation in the industry, which could lead to opportunities to acquire competitors’ assets to rapidly grow market share.
 
Employing machine learning techniques can reveal different segments and opportunities in your customer base and/or market place. This analysis can then be used to create more intelligently designed products and deliver them successfully to your target segments. This holds true for new customer acquisition as well as customer retention and yield management of your own customer base.
 
Can your data analytics team identify any new or underserved segments that with a little thought around your product design, you could quickly access and grow market share? As post crisis markets continue to re-baseline, the structure of products is also likely to drastically change and vehicle leasing companies need to recalibrate to this new baseline.
 
As an example, could subscription based products appeal to more consumers than before? A segmentation exercise could determine this by identifying the profile, needs and expectations of customers allowing you to create new product characteristics such as:

• Low barriers to entry – low or no deposit for customers who can afford the monthly rental but do not have or want to spend a lump sum for traditional 3 to 6 months rental in advance
• Flexible lease terms – on contract duration and/or mileage allowance as well as the option to change vehicles in-life for customers likely to have varying levels and types of use as their needs change
• Credit building – higher risk customers accepted on a monthly subscription based product to build their credit, with a view to switching to a comparable lease contract once they have built up good payment history

 
Having the data and analytics capability to identify these segments and then deriving actionable insight to capitalise on the underlying trends will enable you to target your product development and sales and marketing activity more effectively and provide you with a stronger base for growth.
 
The next part of this series will talk more specifically about Telematics opportunities.

​If you want to know how to use data analytics for sustainable growth in the current climate and beyond, please contact us here to book your free Discovery consultation. 
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Recently, we applied machine learning to a leasing client’s own historical sales data to predict vehicle residual values (RV). Not only did the model identify and quantify pertinent RV risk drivers, it also gave them an empirical basis to challenge industry and manufacturer forecasts.

​One key finding was that for most of the popular vehicle models, the lifecycle adjustment of a well-known industry forecast was too pessimistic. As a result, RVs could have been set higher at inception to achieve a more competitive monthly rental while still being profitable.

However you look at it, managing Risk in today’s environment is critically important to the health of any business but in particular, the Vehicle Leasing sector given the combined impact of both credit and asset risk. Innovative analytical methods such as Machine Learning and a mature analytics capability can play a key role in allowing businesses to continue to grow profitably. Given this, investment in analytical skills and resource should be a primary focus for any Risk oriented business.

The next part of this series will talk more specifically about Governance and Oversight and how a strong data and analytics capability is essential to this.

If you'd like to know more or to understand how we can help you take a more nuanced approach to managing risk, please contact us here to book your free Discovery consultation.
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