Switching Mobile Provider - Doing More With Social
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Ryan Harrison shows us how you can use targeted social analytics to uncover the topics and emotions associated with switching mobile provider.
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Switching Mobile Provider - Doing More With Social
- 1. SWITCHING MOBILE PROVIDER Consumer Insights RYAN HARRISON/ SENIOR SOCIAL MEDIA ANALYST
- 2. Switching mobile provider • This project aims to provide consumer insights into those who are dissatisfied with their current mobile provider or looking to switch. • The findings would give the marketing, social media and outbound sales teams information they need to attract customers who are susceptible to changing provider. NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 3. Insights Central • The project is available in Brandwatch Analytics Insights Central. • You are able to copy this project and many more into your own Brandwatch account. NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 4. Methodology • Query NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 5. Methodology • Query (((at_mentions: (vodafoneuk OR vodafoneukhelp OR o2 OR threeuk OR threeUKsupport OR three OR threeuksupport OR giffgaff OR lebara OR tescomobile OR tescomobilecare OR id_mobile_uk OR talkmobileuk) AND (("want to" OR "wanted to" OR "trying to" OR "tried to" OR "thinking about" OR "have to") NEAR/5 (leave OR cancel OR quit OR switch OR change OR "get a new" OR "in the market for" OR "looking for" OR "want a new")) NOT address)) NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 6. Methodology • Query (((at_mentions: (vodafoneuk OR vodafoneukhelp OR o2 OR threeuk OR threeUKsupport OR three OR threeuksupport OR giffgaff OR lebara OR tescomobile OR tescomobilecare OR id_mobile_uk OR talkmobileuk) AND (("want to" OR "wanted to" OR "trying to" OR "tried to" OR "thinking about" OR "have to") NEAR/5 (leave OR cancel OR quit OR switch OR change OR "get a new" OR "in the market for" OR "looking for" OR "want a new")) NOT address)) NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 7. Methodology • Query (((at_mentions: (vodafoneuk OR vodafoneukhelp OR o2 OR threeuk OR threeUKsupport OR three OR threeuksupport OR giffgaff OR lebara OR tescomobile OR tescomobilecare OR id_mobile_uk OR talkmobileuk) AND (("want to" OR "wanted to" OR "trying to" OR "tried to" OR "thinking about" OR "have to") NEAR/5 (leave OR cancel OR quit OR switch OR change OR "get a new" OR "in the market for" OR "looking for" OR "want a new")) NOT address)) NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 8. Methodology • Query ((at_mentions: (asda OR ee OR talktalk OR talktalkcare OR virginmedia) AND (("want to" OR "wanted to" OR "trying to" OR "tried to" OR "thinking about" OR "have to") NEAR/5 (leave OR cancel OR quit OR switch OR change OR "get a new" OR "in the market for" OR "looking for" OR "want a new")) AND ("mobile contract" OR "mobile service" OR "mobile plan")) NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 9. Methodology • Query (((("want to" OR "wanted to" OR "trying to" OR "tried to" OR "thinking about" OR "have to") AND (leave OR cancel OR quit OR switch OR change OR "get a new" OR "in the market for" OR "looking for" OR "want a new") AND ("mobile contract" OR "mobile service" OR "mobile plan" OR "mobile tarrif" OR "mobile price" OR "mobile provider" OR "mobile network")) NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 10. Methodology • Query (((("want to" OR "wanted to" OR "trying to" OR "tried to" OR "thinking about" OR "have to") AND (leave OR cancel OR quit OR switch OR change OR "get a new" OR "in the market for" OR "looking for" OR "want a new") AND ("mobile contract" OR "mobile service" OR "mobile plan" OR "mobile tarrif" OR "mobile price" OR "mobile provider" OR "mobile network")) NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 11. Methodology • Query ((((site:facebook.com AND ((url:_pageId_355991264476077 OR channel_ids:355991264476077) OR (url:67884984384 OR channel_ids:67884984384) OR (url:38919051253 OR channel_ids:38919051253) OR (url:197259133622381 OR channel_ids:197259133622381) OR (url:87613300716 OR channel_ids:287613300716) OR (url:9615015947 OR channel_ids:9615015947) OR (url:129777533812007 OR channel_ids:129777533812007) OR (url:1604367453123240 OR channel_ids:1604367453123240) OR (url:262012356483 OR channel_ids:262012356483)))) NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 12. Methodology • Query ((((site:facebook.com AND ((url:_pageId_355991264476077 OR channel_ids:355991264476077) OR (url:67884984384 OR channel_ids:67884984384) OR (url:38919051253 OR channel_ids:38919051253) OR (url:197259133622381 OR channel_ids:197259133622381) OR (url:87613300716 OR channel_ids:287613300716) OR (url:9615015947 OR channel_ids:9615015947) OR (url:129777533812007 OR channel_ids:129777533812007) OR (url:1604367453123240 OR channel_ids:1604367453123240) OR (url:262012356483 OR channel_ids:262012356483)))) NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 13. Methodology AND ((("want to" OR "wanted to" OR "trying to" OR "tried to" OR "thinking about" OR "have to") NEAR/5 (leave OR cancel OR quit OR switch OR change OR "get a new" OR "in the market for" OR "looking for" OR "want a new")) NOT address)) NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 14. Rules and Categories • Rules and categories were used to segment the data. NOW YOU KNOW | #NYKCONF BRANDWATCH.COM
- 15. Rules and Categories • Rules and categories were used to segment the data. • Dashboard wide filtering was used to exclude news mentions from the findings. NOW YOU KNOW | #NYKCONF BRANDWATCH.COM Customer Service Delivery Handsets Lost Loyalty Mobile Data Payment Price Reception Roaming Upgrade
- 16. Findings
- 17. 2% 2% 4% 5% 5% 6% 11% 11% 24% 0% 5% 10% 15% 20% 25% Lost Loyalty Roaming Delivery Upgrade Price Handsets Payment Reception Mobile Data Customer Service % Mentions Rules and categories used to give a complete picture of motivations for switching BRANDWATCH.COM
- 18. 2% 2% 4% 5% 5% 6% 11% 11% 24% 0% 5% 10% 15% 20% 25% Lost Loyalty Roaming Delivery Upgrade Price Handsets Payment Reception Mobile Data Customer Service % Mentions Rules and categories used to give a complete picture of motivations for switching BRANDWATCH.COM
- 19. Categories can be used to segment your findings BRANDWATCH.COM Mobile Data Reception
- 20. Categories can be used to segment your findings BRANDWATCH.COM Mobile Data Reception Handsets
- 21. 0% 1% 2% 3% 4% 5% 6% 7% 8% 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% Time of day and day of week analysis can inform scheduling decisions BRANDWATCH.COM Time of day Day of week %Mentions
- 22. Analysis of geo-tagged tweets can help inform campaign strategies BRANDWATCH.COM
- 23. Recap
- 25. 45% 61% 57% 36% 43% 51% 45% 43% 26% 55% 39% 43% 81% 64% 57% 49% 55% 57% 74% 0% 2% 4% 6% 8% 10% 12% 14% TV Animals & Pets Beauty/Health & Fitness Technology Business Books Food & Drinks Family & Parenting Music Sports % Unique Authors Interests & Gender 53% 47% %UniqueAuthors Gender Male Female 1% 1% 1% 1% 1% 1% 2% 2% 3% 5% 6% 0% 1% 2% 3% 4% 5% 6% 7% Politician Legal Scientist & Researcher Health practitioner Teacher & Lecturer Sportpersons & Trainer Software developer & IT Journalist Sales/Marketing/PR Executive Student Artist/Creative % Unique Authors Professions Twitter demographics can provide consumer insights BRANDWATCH.COM
- 26. Insights Central
- 27. This project is available in Insights Central BRANDWATCH.COM
- 28. This project is available in Insights Central BRANDWATCH.COM
- 29. Now You Know
Notas del editor
- Project aims to provide insight into customers who are dissatisfied with their current mobile provider or thinking of switching. What are the main drivers? Why are people unsatisfied with their current network? How does conversation differ between switching motivations? When do people talk about wanting to leave their mobile network? Armed with this information, marketing, social media and even outbound sales teams could swoop in and identify potential customers when they are most susceptible to switching. Structure of the project could easily be adapted for other markets/brands.
- One of reasons I wanted to show you; available in Brandwatch’s Insights Central library Collection of re-built Projects, created by Brandwatch, which present insights from across a variety of topics and industries. You can copy this project and many more into your own account, I’ll show you how at the end. In this presentation I’ll show you some of the really straightforward but insightful components and metrics available in Analytics.
- There were several approaches to this Query to ensure as much relevant conversation as possible was captured, which I’ll take you through Only mentions from the UK in English were captured so the project can focus on providing insight for the UK market.; and I looked at 6 months of data.
- Lots of text on screen; just wanted to give you a sense of the structure of the query and some of the phrases and words we’re looking at to show you how these strings could be adapted for other brands. What I’m about to take you through isn’t the exhaustive full query, but just some snippets to give you an indication of how it was built.
- To start, I established the relevant Twitter handles in this first section
- And these were searched for a variety of keyword strings aimed at teasing out dissatisfaction, such as ‘want to’, ‘wanted to’, and the desire to leave the network.
- Some providers, as you can see now, did not have handles specific to their mobile offering, some of them were supermarkets, so these brands had to have their own 'mobile service' qualifying terms added to their search strings.
- In addition to that brand-lead approach, the query also gathered mentions from people who were either stating the desire to leave ANY
- mobile provider/service, or take out a new contract or those who were expressing dissatisfaction with their current service.
- In this part of the query we’re looking at the Facebook pages of the mobile providers
- that’s what all of these codes are, which you can find by Googling Find My Facebook ID and inputting the URL you want to track. You must also set up the relevant Facebook channels within the project;
- so we’re searching those public FB pages on the previous slide, for these words, i.e for people wanting to cancel. So hopefully you can see the structure of this query could be adapted for a variety of industries by switching the brands and the vocabulary.
- Then rules and categories were used to then segment the data; for anyone in the room who is not a Brandwatch user. rules are searches within a category we use to segment the data and categories are the labels we then give to those segments, The objective was to get a deeper, data lead understanding of why people want to switch contracts. In order to start segmenting the data, I looked at a topics cloud of all the mentions. As we can see, customer service was emerging as a dominant topic; so I wrote a rule which took into account all the variations of the ways people say customer service; I then excluded those mentions from the topics cloud by filtering..
- We can then see that signal and reception are emerging as dominant topics, so I wrote rules to categorise those mentions and I kept repeating the process, also using some topics which I established manually reading some of the mentions, to chip away at the uncategorised mentions until the majority had been categorized. And this is the final list of categories which were generated, so we’re looking at people talking about handsets, delivery issues, payments, reception, their mobile data etc. As only consumer conversation is of interest, General and News Page Types were excluded from every tab using the handy 'Apply Filters to all Components' button at the top of the dashboard.
- So let’s now take a look at some of the findings all of this setup revealed
- As we saw from the original topics cloud, customer service is the main reason customers are dissatisfied with their mobile provider. Customer Service was mentioned in almost ¼ of conversation. This finding could be influenced by the nature of social media, insofar as it is as used as a customer services channel; however if mobile providers are able to speak to customers with positive statistics about the satisfaction levels customers have in regard to service, this could form the basis of a solid 'customer poaching' strategy. Mobile Data and Reception come in 2nd and 3rd place, showing that the fundamental elements of a mobile service are important to people. However, when compared with a motivation such as Price, in 9th place, which was surprising to me, advertising/marketing around this fairly fundamental attribute of a service might not strike as much of a chord with 'switch ready' consumers as being able to talk about customer service. And the same goes for upgrading or roaming.
- The mentions which hadn’t been categorised were excluded from the chart on the left using filtering, but are shown in this topics cloud.. Mentions such as 'what number do i ring if i want to cancel my contract?', although not deeply insightful from a marketing or social strategy point of view, still provide a social selling opportunity. Discussion around obtaining a PAC code, again, provides a direct opp to those who want to switch. Mobile providers could swoop in on these mentions with a direct reply, offering their services.
- Using Categories as a base, it's possible to segment your data in lots of, ways using Brandwatch Analytics. The Topics component has been used to look at key themes emerging from within the Mobile Data and Reception Categories. This was done using straightforward Category filtering on each cloud. The distinctions between the three clouds demonstrate how the topic clouds can simply display emerging themes to provide clear insights. For example, promoting your network's strong data coverage in London would resonate well with customers, who talk about it frequently. And if you were a competitor to O2, you would be wise to know their reception often comes under fire.
- This topics clouds identified the topics handsets people are talking about most when they want to switch, so If you had a deal going on Samsung Galaxy handsets, then you could use the cloud right to target those customer who just might be in the market for one.
- The two charts show the times of day and days of week when mobile provider switching chatter occurs. These charts are really simple to build but provide valuable insight. They are using all mentions in our query and look at either 'Hour of Day' or 'Day of Week' which can be selected in the chart component’s filters and give you a digestible view of when conversation is taking place. If you were looking to run a team of social media agents to directly contact and lure switchers, for example, between 4pm and 8pm would be the time to do so. Similarly, if you were looking at running any advertising/marketing campaigns, you would be wise to know that Thursday is the most popular day that customers take to the social web to talk about switching; Friday and the weekend not so much.
- This map show the location of geo-tagged tweets, with the bluer larger circles denoting the density of tweets. This component is easily added under the ‘Maps’ option on Analytics. The map has been filtered to those tweets which are attributed to the 'Reception' switching motivation, so we can see a high density of Tweeters talking about poor reception coming from London and the South East; which fair enough, is where the greatest population density of the UK occurs, so this is to be expected. However, if you were looking at a Twitter rich dataset and were looking at placement of advertising, for example, these maps components can help inform where you’re likely to be able to reach people who are likely to be talking about the product or service you’re offering.
- Let’s just take a recap of what we’ve learned about our mobile switchers so far: Customer Service is the largest motivation for switching, with people also disgruntled about reception and mobile data. People tend to to complain about price so much. London is a key area for dissatisfied conversation about reception and mobile networks. People talk about wanting to switch mobile providers mostly between 4-8pm, and in the week rather than the weekend.
- Built in to BW analytics are Twitter demographics, which can give you Insight into the profile of the tweeter who are generating your conversation; so let’s take a look what we can learn about those people who are wanting to switch mobile provider
- So let’s start with Gender, our mobile switcher data-set has a slight male bias. What’s nice about this chart, and the 2 we’re about to see, is that they are set to count ‘Unique Authors’ rather than the volume of mentions, so we’re actually looking at stats based on the number of people rather than the number of tweets. Now let’s look at professions; this data runs off automated rules which read through individuals’ Twitter bios to categorise them into certain profession types. Our potential mobile switchers are most likely to be in a creative professions, or a student. Looking at interests, the key interest for our audience is Sports, followed by Music and Family & Parenting. I’ve actually grouped this chart by gender too, so if you were wanting to target women we can see that technology might not be an interest which appeals, but food & drinks or animals & pets might.
- So that concludes the insights we gleaned from our mobile switchers’ conversation and authors. I just want to round off by showing you how you can access this project and many more.
- There is a link on the Welcome to Brandwatch homepage
- Or at the top of the platform when you’re within a project. Once you’ve loaded a project, click ‘Get Full Control’ to copy it into your account, to have full control, take inspiration from the queries, rules and dashboard set-up and adapt any of the projects to your own purposes.
- That concludes my presentation today, if you have any questions then please don’t hesitate to find me on the floor or on the research pod. I hope you enjoy the rest of the day, and I’m now going to introduce my colleague Arthur.