O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.

Mobile Marketing - Consumer journey use cases

I have covered details around Mobile Marketing, and structured my presentation

1) Landscape
2) Dollar Flow in the AD Ecosystem
3) Traditional VAS Marketing Methods for Feature Phones
4) Mobile Marketing
5) Automation of Campaigns based on Data - Machine Learning
6) Investor Interests in the Space
7) Traditional Cookie Data from Traditional Browsers

Livros relacionados

Gratuito durante 30 dias do Scribd

Ver tudo
  • Seja o primeiro a comentar

Mobile Marketing - Consumer journey use cases

  1. 1. Mobile Marketing ANSHU SHARMA – IIFT 17TH JAN 2015
  2. 2. Scope  Traditional Telco Mobile Marketing – Feature Phone Marketing  Channels  Gratification Business Case  Mobile App Marketing  Channels  Suggested Example Video  Mobile Market Automation  Segmentation, A/B Testing,  In App & PNs Campaigns  Analytics & Data  Ecommerce Market Tracking Sheet & KPIs  Deep Linking  Cohort Analysis
  3. 3. Mobile Ad Landscape
  4. 4. * Includes: Ad Networks, SSPs, Private Exchanges, and Ad Rep Firms ** Includes: Media Agencies, Creative Agencies, Trading Desks *** Includes: Ad Exchanges and DSPs This slide represents Display, Video, Mobile – based on Rare Crowds Analysis of Advertising Ecosystem Impression / Dollar Flow Note: Ad Servers, Yield Management Systems, Analytics, DMPs, etc… Take out about 8-10% of spend in total as they pass through the ecosystem 20% ADVERTISER AUDIENCE Agency* * Keeps 5-25% Large Publisher (Direct Reserved “Premium ”) Keeps 75-95% Large pub (Remnant) Keeps 40-80% Publisher Aggregat or* Keep 10-80% (May include multiple vendors) Small- Med Publishers Keeps 20-60% 65% 25% 5% 20% 25% 25% FLOW OF ADVERTISING IMPRESSIONS (US) Percent impressions 40% 35% 40% 35% Ad spend 100% Exchang es, DSPs *** Keeps 10-20% (may include multiple vendors) FLOW OF AD SPEND (US) Percent Dollars Captured (in yellow) 25% 5% PerImpressionPriceDrops 4
  5. 5. Communication Platforms for VAS Promotions VAS PROMOTIO N PLATFORMS SMS Out Bound Dialing EOCN/USS D CELL INFO PLC Cross Promotion IVRS SELF CARE START- STOP RETAIL/ Go to Market
  6. 6.  Most Popular Communication Platform  Advantages – Cost effective , Fast n Easy & Reliable  Perception – Considered intrusive, irrelevant  Can be leveraged by  Base Segmentation  Vernacularisation  Timelines  Over 65% customers prefer to read promo SMS on Sunday/Holidays; Weekdays are not proffered especially Monday & Tuesday  Customers would also prefer Promo SMS after 5 PM SMS
  7. 7.  Tagging Promotions on Activation and Deactivation messages across related / unrelated product categories  Behtar Zindagi 55678 IVR be promoted as a tag promo on Deactivation message of “Mitti Ke rang”  Behtar Zindagi promo can be tagged with on MT Charging SMS of Talk to Me  Tagged promo on Activation Confirmation of a related / untelated VAS category across other VAS Content providers.  Behtar Zindagi Can be tagged on LAPU Conf SMS on IFFCO Base  Explore possibilities of tagging promo on HT Song Change Confirmation SMS to Rural base  Tagged Promo on Service messages - Roaming ; Users usually read sms received during travelling and a far likely to try a service  AFC from Altuist can be promoted on Drop over message on Raat Baki IVR of Hungama  Friend Locator is likely to get better conversion if tagged with PLC content of Friends Chat. PLC Cross Promotion
  8. 8.  POP Material in the rural market. Nearest Grain/ Vegetable Markets.  Incentivize Retailer on activation of VAS through PEF/SEF  POP Material at IFFCO Retailers for VAS Targeted at Rural base  POP Material at Youth Hangouts for Youth centered VAS  Festive gatherings, Religious gatherings, Agro Fests or and related events attended by rural folk  Branding on Public transport  Branding on Public carriers  POP at Weekly Bazaars  Celeb Endorsements on radio and RJ mentions  Radio Contests  Cinema Advertising / Brand  Branding on Transport used by FOS and Incentive on conversion  Buzz on Social Media through Celeb endorsements, Winners of Various Contests RETAIL/ Go to Market
  9. 9. Telco/VAS Marketing Plan: Gratifications Operator Subscriber Base of UPU Airtel 18,055,029 Airtel Rural Base in UPU 9,930,266 Avg Price point @ Rs15, Total Revenue/ Month 72,300 1,084,500 Activity Conversion Number/day USSD Promotions,4 hrs/ 30,000 300 Cell info, 1 week/ 2,40,000 1,000 SMS push of Toll number - 1 lacs sms for 1K calls 100 Ground Activity - 10 OBD from Airtel End: 300,000 => 4000 Subs 1,000 Monthly Subscription 72,300 Total Airtel Revenue 1,084,500 Company Share @ 36% - Earning 0 Gratification Cost 10% of Handygo share 0 Company Share 0 Projected Subscriber Monthly Projected Revenue (INR) 289200 2,892,000 Airtel Share Share @ 66% 1,908,720 Company Share @ 36% - Earning 983,280 Gratification Cost 10% of Handygo share 98,328 Company Share 884,952 Operator Subscriber Base of UPU Rural Base in UP 1st Month Penetration 2nd Month Penetration 3rd month penetration 4th month penetration 5th month penetration 6th month penetration Airtel 18,055,029 9930265.95 29791 49651 69512 99303 129093 158884 Avg Price point @ Rs20 595816 993027 1390237 1986053 2581869 3177685
  10. 10. Mobile Marketing Non Feature Phones  The Telco managed mobile Marketing –  SMS - Short Codes  Outbound Dialer – Long Codes  USSD – Customer Self Care Portal - *121#  Cell-id based Broadcasts  IVRs  Missed Call Alerts  In Message Advertisement An Example – Behtar Zindagi
  11. 11. Mobile Marketing  Mobile Website / Native App strategy  Location Based Marketing  Permissions / Preferences
  12. 12. Appstore Optimization : ASO  Keyword optimization (KWO)  Keyword Optimization (also known as keyword research) is the act of researching, analyzing and selecting the best keywords to target and drive qualified users from app stores to your app. App store optimization tool provider Sensor Tower also breaks keyword optimization down into three parts: relevance, Difficulty Score and Traffic Score.[16]  Conversion rate optimization (CRO)  Conversion rate optimization involves all metadata available and publicly accessible in the app stores, like icons, screenshots, description and update texts.[17][18] This part of app store optimization is responsible to convert the traffic acquired through keyword optimization into app downloads.  Location labs Appstore Optimization Slides
  13. 13. The Keys to Mobile Marketing  Usability  Do you have a mobile optimized site?  Do you have a native mobile app?  How many steps from search to purchase?  Trust  Do you have a user rating?  Are people talking about your brand online?  Personalization  Are you finding you customer at the right time?  Are you reaching your customer in the right place?  Are you targeting your customer with the right message?
  14. 14. Customer Engagement & Retention  Push notifications  In-app messaging  Deep-linking  Gamification
  15. 15. Mobile Marketing Campaign Gist
  16. 16. WOWOKAY, SO HOW AM I GOING TO DO THAT ? DO WE HAVE A DEDICATED MAN SITTING BEHIND AND OBSERVING LIKE WE ARE DOING IN THIS VIDEO?
  17. 17. Challenges  Location – Where am I ? Office or Home or my shop ?  Personality/ Personas – If I love to camp or play football ? Which Segment?  How do you know my existing products, my brands and what needs replacement ?  How do you know if I look for expensive brand or a less priced ?  How do you know that I would love to fly as a second alternative ?  How come an app knows that I could make payment through my mobile?  What happens to my privacy ?
  18. 18. How is a mobile marketer going to make all these rules?  Should I map the Customer Journey ?  Event based Rule engine should do ?  How many such rules would be required to monetize the moments of truth ?  Is it feasible ? YES  Lets Make some Rules - Exercise
  19. 19. Mobile Market Automation Aspects  Segmentation  PUSH  In App Campaigns  A/B Testing  Analytics & Data
  20. 20. Automated A/B Testing  Experimenting Options Available w.r.t every touch points with the customer in terms of  Message  Content  UI elements  Run the experiments on consumer segments to target based on Location, device type, app version, traffic source, and in-app behaviors.  Add your own Attributes and events specific to the particular brand app  Running concurrent experiments on the Fly
  21. 21. Event-Based Triggers • Behvior Triggers • Add to Card • Card Abandonment • Checked a Product /discount items • Has Shared socially with friends • Life cycle Based • New User • Repeat Customer • About to Churn • Calendar Events • Real Life Event • Instant Gratification • Entering a Mall/Shop
  22. 22. Geofence and iBeacon Triggers  Wifi Finger Printing  iBeacon Indexing  GPS/Cell-Id
  23. 23. custom in-app message templates to drive higher user engagement.
  24. 24. Dynamic Targeting Send highly personalized messages to • specific customer segments • geography, • device type, app version, • traffic source, • custom user attributes, and in-app behaviors
  25. 25. Native Ads – How and ROIs  TBD  For Reference – Snap Deal Marketer  http://appiterate.com/usecases.html
  26. 26. App Widgets – Mobile Screen Real Estate  TBD
  27. 27. Ecommerce Marketing Tracker  Marketing Spend:  Visitors to the website/ Mobile /channel  Orders / channel  Orders / Customer: How many orders does a customer submit, who was first attracted through this channel. This KPI is influenced by other factors as well, but gives you an initial feeling for the customer quality. It is up to you how you define “lifetime” (1m/6m/1y). In a later version I will go into CLV management more deeply.  Revenue/ channel  Discounts Needed/ Channel
  28. 28. Are these KPI Useful ? What do I do now?  Basket Size (Basket): How much revenue did this channel generate per order. It helps you to understand the economic value of the customers that you attract through the different marketing channels.  Conversion Rate (CR): How many customers per 100 visitors, that came to your site, finally ordered a product? The Conversion Rate helps you to understand if people that came through this channel only browsed around, or actually purchased something.  Cost Per Order (CPO): How efficient is this marketing channel? The cost per order tells you how much you spent to generate one order.  Real Cost Per Order (real CPO): To be able to compare channels on a CPO basis (which channels generates the cheapest customers), the CPO should be adjusted by certain factors. One big influencer is the discounts you needed to give to generate an order. E.g. flash sales are usually relatively cheap to initiate, however you need to give huge discounts. This increases the adjusted (real) CPO accordingly.  Customer Lifetime Value (CLV): How much revenue does a customer that is generated through this channel generate for you. This factors in the average basket size, as customers that come through different channels reorder differently, and spend different amounts.  Return On Marketing Investment Factor (ROMI Factor): How much revenue did you generate per dollar invest? It helps you to understand the return per marketing channel. It is already adjusted with discounts to compare the factor cross channel. If you want you can adjust the factor by other influencers.
  29. 29. Exercise – Abandon Cart?  How do I retarget ?  Rules  Time Duration after someone traversed and left  Message Method  Discount  Segment  Timing
  30. 30. Attribution Modeling A NECESSITY FOR CONVERSION STRATEGY
  31. 31. Last Touch Model  Measure - What was the last touch point the customer interacted ?  What is the conversion % for each last touch point ?  Generally, Email is considered the greater impact for sales conversion
  32. 32. First touch Model  Measure – What % of the consumer use which channel as First touch or the first interaction or the first keyword (in direct search)  What is the impact % table for each touch point in FTM ?  FTM works as a strategy tool for Social Media marketer
  33. 33. Equal weight Distribution  Natural search %  Comparison Shopping % - A-B/Split Testing & Strategy  Banner %  Email %  Interstitial  In App  Push Notifcaion Measure – Resultant Comparison
  34. 34. Customer Credit Sharing  Engagement Factors  Click on Ad  Simply Viewed  In Video Banner Ad  Media Factor  Std Ad  Animated Ad  Size of the Banner Ad  Time Factor  Time span between ads viewing & conversion  Position Factor  Position the offer/ad conversion %
  35. 35. Attributions Model -Campaign Measurements & Results  Which Ad channels drive the best results ?  How are these channels influence each other ?  Which mix of ad/market spend on these channels works best ?  What kind of creative ?  What size, placement & frequency drives the consumer behavior ? Basically what tactics works best for your business !
  36. 36. Custom Credit Rule Examples  What are the customer credit Rules that works best and leads to conversion.  It could be a series of interactions before the moment of truth  Mapping the Offer/Ad Interaction Maps and conversions provides the best media/campaigns budgets.
  37. 37. GA – Just for Reference
  38. 38. Measuring Mobile Marketing Success  Campaign Measurement / Channel  Cohort Analysis
  39. 39. Sample Cohort Analysis
  40. 40. Analyzing the Cohorts On Boarding Trend, the orange left arrow, indicates the product’s effectiveness in its first month of use and its trend over time, which is nothing less than a metric for user on boarding effectiveness. The first cell in each column indicates the monthly active rate for the cohort’s first month as users. In our hypothetical data set, that number’s growth varies from 35% to 41% over time. The product team has done a reasonable job of improving user on boarding and engaging users when they sign up Longitudinal Trend, the top red arrow, indicates how the activity rate changes as users continue to use the product. The first row is the oldest cohort of users with the most recent data, the ones who signed up most recently. The bottom row is the newest cohort. Time flows right in this chart.
  41. 41. Cohort Analysis  Average Revenue Per Customer Over Time - Chart monthly revenue over time to contrast with cohort data  Individual Channel Growth Over Time - Chart all accounts to visualize trends.  Number of Customers in Each Cohort - Chart number of customers in each cohort to see how sensitive cohort data is to sample size and also see the size of the new customer pipeline over time.  Average Monthly Revenue By Cohort - Chart the revenue by cohort to see if newer customers generate more or less revenue than older customers. Really good for marketing spend evaluation.  Cohort Comparison - Chart the different cohorts over time to see how their revenue characteristics compare.
  42. 42. Why Deep Linking Matters ?  Links in email/sms not prompting the user to open the native app and straightway opening a browser Quite prominently seen in most of the apps  Ecommerce Alerts providing the Access to Purchase, however not taking directly to Purchase page or CTA landing page  Asking to login on a portal – Ebay and not taking to PDP  ETSY has fixed it and fixed the revenue leakage as well  Marketing links automatically detects the presence of app and auto- matically takes to either browser or app  Gaming Apps can save the session today, however they can include the saved sessions in the PN and straightway take it to the that level  In App / PNs directly translate into revenue  TW Cards / Google App Indexing / FB App Link Tags
  43. 43. Deep Linking is Context Critical for Mobile Marketing  ADs 2 App  SMS 2 App  QR 2 App  EMAIL 2 App  WEB 2 App  SOCIAL 2 App  App 2 App Landing Page Optimization Strategy per traffic source. Cohorts / Traffic Source would enable to fix the leakages and marketing campaigns improvement
  44. 44. Good Read on Deep Linking
  45. 45. Investment Landscape
  46. 46. Merging the Old & the New World  Browser Cookie Data Management - DMP
  47. 47. Data Management Platform ( DMP )  A very smart, very fast cookie warehouse with analytical firepower to crunch, de-duplicate, and integrate your data with any technology platform you desire  Demdex (now owned by Adobe), RedAril, and Krux are what I would consider pure-plays, while Lotame, Collective, and Turn provide services
  48. 48. DMP Information flow
  49. 49. Multi Channel Data Aggregations@DMP
  50. 50. Cookie Match Companies  LiveRamp,  DataLogix,  Datran,  TargusInfo,  Acxiom
  51. 51. Ad Cookies – Step by Step  A request is sent from the browser to retrieve an ad from the publisher network.  The publisher network retrieve a number of variables from the publisher - in some exchanges the publisher chooses which data it wants to share. Publisher variables include: Anonymity (if anonymous URL above is not shown) URL e.g. youtube.com CookieId (buyers can use this to match the user to a pervious seen user, e.g. for a remarketing campaign) Vertical - e.g. Videos > Sport Blocks - e.g. no Google Chrome ads please Location - e.g. user is in UK, London 
  52. 52. Ad Cookie – Step by Step …  The network then sends out a request to the buy-side to find ads e.g. a request is sent out to the DSPs and Buyer Networks. This includes the publisher variables that are set in the request sent to the exchange.  The DSPs and BuyerNetworks then run a query. SELECT snippet, bid FROM all_advertisers WHERE targeting_url = request_url  Bids are then returned by the buyers within the 120ms threshold.  bidder_A: Advertiser: amazon.com CPM: $2.50 bidder_B: Advertiser: ebay.com CPM: $0.50  The winning ad is then selected. The ad is sent back to the users browser. The buyer often pays the second highest price to the buyer.
  53. 53. Real Time Bidding  User visits a website: say abcd.com.  Within abcd.com there is a HTTP request to SSP, to fill an ad slot.  On receiving the request for showing the ads, the SSP conducts a real time auction. To each of the DSPs whose have expressed interest in this user (some SSPs may be willing to show ads to users from a specific geo, some may be willing to show ads on a certain website, a retargeting DSP may be willing to show ads to a predefined set of users etc.)  The SSP sends a bid request. The bid request looks like this: [ "auction_id": 1234abcd, "geo": "Bangalore, India", "ad_width": 728, "ad_height": 90, "website": "abcd.com", "id": ssp1234 ]
  54. 54. Real Time Bidding – Demand Side ..  On receiving the bid request, each DSP needs to send a bid response. DSPs typically calculate bid response based on the parameters in bid request (geo, banner size, etc) and the user profile that DSP has stored for user id dsp1234 (Remeber that ssp1234 was mapped to dsp1234 in cookie mapping stage, and data provided by DMPs is stored against the key dsp1234) Bid response looks like this: ["auction_id": 1234abcd, "bid_value": 12.34 "adTag": "<script type='text/javascript'> document.write ("<script type='text/javascript' src='dsp.com/showad'"); document.write ("</script>"); </script>" ]
  55. 55. Real Time Bidding – Awarding the Inventory  The SSP compares the bid response of each of the DSPs, and awards the impression to the highest bidder. These auctions are usually second price auctions: the highest bidder wins and cost to highest bidder is second highest bid in the auction.  The SSP redirects user browser to the ad tag provided in the bid response, which renders the ad to the user's browser.
  56. 56. APPENDIX
  57. 57. Reference Digital Marketing Periodic Table for Lingo & KPIs
  58. 58. Players •Adobe Marketing Cloud Appboy •Appiterate •Artisan •DeltaDNA •Kahuna •LeanPlum •Localytics •Nudge •Playfab •Playnomics •PushSpring •Scientific Revenue •Silverpop •Swrve •Tapjoy (via 5Rocks) •Tapcrowd •Upsight (formerly Kontagent) •Urban Airship  Which mobile automation features do you use? *This question is required.  Targeting  Cohort analysis/user profiles  Segment targeting  Retargeting app users outside of your app  Targeted in-app rewards or incentives  Analysis  A/B and/or multivariate testing  LTV tracking  Real-time analysis  Surveys, ratings, feedback  • Messaging • Push notifications • Email campaigns • SMS campaigns • In-app messaging • Location-specific behavior/messaging • Optimal time, delayed, or flexible messaging • Engagement • Personalizing app content • Personalizing app functionality and/or gameplay • Promotions and/or sales •
  59. 59. Acronyms  Eng. Rate Engagement rate. For promoted tweets on Twitter, engagement rate is calculated by dividing the number of engagements a promoted tweet receives by the number of impressions.  Follow Rate For promoted tweets on Twitter, follow rate is calculated by dividing the number of follows by the number of impressions within a campaign.  Form Submits Form submissions on a website. These metrics indicate what percent of form submissions come from each digital marketing source (e.g. paid search and referral traffic, email campaigns, social media).  Gross Open Rate The number of times an email message is opened, either by the original recipients or by those to whom the recipient forwarded the message, divided by the total number of delivered messages. Also known as total open rate.  Like Rate Facebook page like rate. The number of page likes divided by number of impressions per ad.  MQL Marketing-qualified lead. This is a lead that Marketing has vetted and passes on to Sales.  RL Raw lead. This is a lead that has not yet been vetted and accepted by Marketing.  SQL Sales-qualified lead. This is a lead that has been passed on to Sales from Marketing, and accepted by Sales.  Unique Open Rate The number of unique recipients that opened an email message divided by the total number of delivered email messages. This measure does not count multiple email opens by a single recepient
  60. 60. Thanks Anshu.bakshi@gmail.com 9910695016

    Seja o primeiro a comentar

    Entre para ver os comentários

  • MichaelRausher

    Feb. 9, 2016
  • SeanMcInally

    Feb. 15, 2017

I have covered details around Mobile Marketing, and structured my presentation 1) Landscape 2) Dollar Flow in the AD Ecosystem 3) Traditional VAS Marketing Methods for Feature Phones 4) Mobile Marketing 5) Automation of Campaigns based on Data - Machine Learning 6) Investor Interests in the Space 7) Traditional Cookie Data from Traditional Browsers

Vistos

Vistos totais

1.001

No Slideshare

0

De incorporações

0

Número de incorporações

17

Ações

Baixados

22

Compartilhados

0

Comentários

0

Curtir

2

×