8 marketing lessons from the AAP’s stunning victory in the Delhi elections

The Aam Aadmi Party (AAP) didn’t just win Delhi on Feb. 10. It triggered an electoral landslide and amassed 96% of the seats.

In marketing terms, a relatively small brand just captured almost the entire market by dislodging a super brand—in this case Narendra Modi.

So, how did brand AAP manage this, and what lessons can marketers learn from this?

1. Don’t believe your brand is infallible

A classic mistake that marketers make is to believe that their brand is infallible. Marketing graveyards are filled with brands that made this error in judgement.

Brand Bharatiya Janata Party (BJP), after its massive success in the 2014 general election, fell victim to the same belief. It assumed—wrongly, in hindsight—that they were infallible given that they had dislodged the Congress completely.

Marketing history is also replete with numerous case studies of smaller, nimbler startups completely dislodging stodgy established companies and transforming themselves into market leaders. In the 2015 Delhi election, the two-year-old AAP did just that.

2. Understand your consumer’s needs

Businesses succeed mainly because they listen to their consumers. Marketers who take the trouble of conducting frequent market visits—and talk to retailers, consumers and distributors—have a better sense of what the marketplace looks like.

Something for everyone.(AP Photo/Altaf Qadri)

For brand AAP, it was no different. Through Mohalla Sabhas and Delhi Dialogues, their representatives spent most of the pre-election period keenly understanding what the common man’s key issues and problems were. By virtue of listening to their voter base, the AAP was in a far better position to address these issues as part of their election campaigning. And that strong connection paid significant electoral dividends.

3. Your product should address your consumer’s pain points

There is a reason why fairness creams exist in the Indian marketplace. Marketers have understood the psychological pain points of those who are less fair and addressed them by positioning their brand as the panacea.

In the same way, the AAP got the pulse of Delhi and crafted a manifesto that provided solutions to a wide section of voters. From free water to subsidised electricity bill, from toilets to free Wi-Fi, they had something for every class of consumers.

4. Don’t overexpose your brand messaging

Another classic trap that many marketers stumble into is overexposing a brand ambassador and the brand line.

The consumer fatigue curve is short when it comes to advertising messaging, which is why creative agencies have to continuously think of new ways to bring excitement to the brand. Short, sharp bursts of communication are recommended to reduce brand messaging fatigue, especially when one tends to use the same advertising line in every campaign.

For the BJP, the Delhi election was an overexposure of their brand messaging. Unlike other markets, Delhi’s consumers are exposed to campaigns that happen elsewhere in the country thanks to 24*7 media. So, the same winning campaign that delivered rich results since the 2014 polls in other states simply crumbled in the national capital.

5. Have a relatable brand ambassador

Brand AAP had an extremely relatable brand ambassador in Arvind Kejriwal, and they nailed it with his symbolic cap and the earthy muffler.

On the other hand, the BJP had to project two brand ambassadors—prime minister Modi and Kiran Bedi. Ignoring the fact that the brand ambassador has to be relatable to the geography and market, the BJP played up Modi rather than Bedi. And of course, it didn’t help that Bedi was far less media-savvy than her rival, Kejriwal.

6. Take your trade along with you on your journey

Often, the key to building a great brand is pulling along trade partners and the distribution channel. That’s because a good distribution channel and motivated trade team guarantees about half of a brand’s ultimate success.

In the Delhi election, the AAP had scores of highly motivated volunteers from all over the country to campaign for the party. The BJP, meanwhile, had to crack the whip and bring in their heavy hitters alongside thousands of workers to combat the channel clout of the AAP.

In trade marketing, it is often the foot soldiers—the cycle salesman, the sales executive, the distributor’s salesman—who win the small battles in every locality rather than the senior management team that comes in for market visits.

The AAP’s charged-up volunteers were clearly more than a match for the BJP’s top generals and hired workers.

7. Have a great marketing team

It’s a fallacy to believe that one man is responsible for a marketing success. It is rarely so.

  When tl outcome. 

While one man can drive the vision, it takes a team to implement that vision, handle the operational logistics flawlessly and work on the day-to-day detailing.

The hiatus between the 2014 general election and the 2015 Delhi polls gave the AAP enough time to put together a team that could work cohesively.

For the BJP, it was a struggle until the last minute to put together an effective team. Most decisions appeared to percolate from the BJP headquarters rather than the state unit. When there isn’t a great team, it’s pointless to expect an exceptional outcome.

8. Don’t expand until you’ve got steady revenues

When the AAP was launched as a national brand in early 2014, it failed to make a dent. This, despite massive media attention that the brand had got across national television and print.

That’s because as marketers, the AAP made a rookie mistake—expandin

The result of that marketing move is now evident, and how.

all content from : yahoo.com

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What is cohorts analytics ?

About cohorts

Examine the behavior and performance of groups of users related by common attributes.

A cohort is a group of users who share a common characteristic that is identified in this report by a Google Analytics dimension. For example, all users with the same Acquisition Date belong to the same cohort. The Cohort Analysis report lets you isolate and analyze cohort behavior.

In this article:

See cohort data

Ways to use cohort data

Next steps

See cohort data

The Cohort Analysis report is available for properties using Universal Analytics. No changes to the tracking code are necessary.

To see cohort data:

Sign in to Google Analytics.

Navigate to your view.

Select the Reporting tab.

Select Audience > Cohort Analysis.

Ways to use cohort data

Cohort analysis helps you understand the behavior of component groups of users apart from your user population as a whole. Examples of how you can use cohort analysis include:

Examine individual cohorts to gauge response to short-term marketing efforts like single-day email campaigns.

See how the behavior and performance of individual groups of users changes day to day, week to week, and month to month, relative to when you acquired those users.

Organize users into groups based on shared characteristics like Acquisition Date, and then examine the behavior of those groups according to metrics like User Retention or Revenue.

Organise users into groups, and compare group performances.

The Cohort Analysis report lets you get a more accurate picture of your relationship with users. With this report, you can organise users into groups based on shared characteristics, like acquisition date. This lets you analyse and compare the behaviour and performance of different cohorts across a variety of metrics, like User Retention and Revenue.

Understanding cohorts

Cohorts are sets of users organised into groups united by a common element and a time frame. In the Cohort Analysis report, this element is a Google Analytics dimension or an attribute of the users. The time frame can be a Day, Week or Month. For example if the cohorts are organised by the dimension Acquisition Date and the time frame Day, then all users in each cohort started their first session on the same day.

Cohorts let you see how users in one group compare to each other, and it also lets you compare behaviour across different groups. Use this report to help you better understand which app versions, which features and content, and which ad campaigns attract more long-term and more frequent users.

For example, you could use this data to see if the percentage of users that had their first session last month continue to return to your content more (or less) than users that you first acquired this month.

Reading the Cohort Analysis report

You can find the Cohort Analysis report in the Audience section of a reporting view.

You can only analyse one dimension and one metric at a time in this report. Use the Cohort Type menu to select the dimension, and the Metric menu to select a metric.

Rows represent cohorts. The size of the cohort is listed as a whole number in the margin of each row in the Cohort Type column in the data table.

Columns display the Cohort Type and the number of days that have passed since the event that is the organising element of the cohorts. Day 00 is the day on which all members performed an action to be grouped together. For example, if the cohorts are based on the Acquisition Date, Day 00 is the day all users started their first session, Day 01 is the day after the first session happened, etc.

Cells display the number of users that belong to that cohort and the corresponding time period. The colour intensity in each cell visually indicates the percentage of users relative to the total population of that cohort. A higher percentage is represented by a darker shading. You can also hover over a cell for details.

The top row of the table displays the average retention across All Cohorts. Any changes that you make to the data view (like changing the Cohort Date Range) are reflected in that average.

You can change the data displayed in the report by selecting different options from the menus that appear above the data table.

This report supports both default and custom Advanced Segments. Each Segment appears as a separate data table in the report. You can compare up to four segments at a time

Configure the report

Menus you use to configure the Cohort Analysis report

Use the menus to select:

• The dimension that characterizes the cohorts (Cohort Type)
• The size of the cohorts (Cohort Size): You determine the size of the cohort by selecting the value type for the dimension. For example, if you determine the cohort by the dimension Acquisition Date, you can change the dimension value type to day, week, or month. With these settings, a cohort would be all users who were acquired on the same day, or during the same week or month.
• The metric you want to evaluate (Metric)
• The relative date range of the data, and the number of cohorts (Date Range)
• Which cohorts are illustrated in the chart (N selected)

Understand the data

Report configured to show Acquisition Date cohorts by the User Retention metric

Chart

By default, the chart shows the cumulative metric values for all cohorts. Use the N selected menu to select a cumulative chart line and/or chart lines for individual cohorts.

Columns

The first column identifies the cohorts and the number of users in each cohort. For example, if the dimension by which you characterize the cohorts is Acquisition Date, this column lists the acquisition date for each cohort, and the number of users you acquired during that time frame (day, week, month).

The rest of the columns reflect the time increments you choose for Cohort Size. For example, if you select by day, then each column includes one day of data. There are 13 time-increment columns, 0-12.

Rows

The first row shows the total metric value for all cohorts for each column. For example, if the metric is Pageviews and the columns are daily data, then the first row shows the total pageviews for the day.

The other rows show the values for the individual cohorts.

Cells

The cells for time increments 0-12 hold the relevant metric values. For example, if you are using the Pageviews metric, then each cell contains the number of pageviews per cohort per time increment.

Segments

When you apply Segments to this report, the data for each Segment is displayed in a separate table.

Examples

Micro trends

Examining the micro trends that in aggregate constitute your macro trends can give you a more realistic picture of your business. For example, your quarterly data might show a steady increase in transactions over that period, which you would regard as a positive outcome. If, however, you were to examine the weekly cohorts that make up that larger data set, you might find that while an overall influx of new users is contributing to a growing number of transactions, there is a regular, dramatic decline in transactions after week 5. Now you know exactly when to reengage users (week 4) in order to improve the performance of each micro trend, and thereby multiply the effect on your macro trend.

Consistency, improvement, or deterioration across cohorts

By simply comparing the values in a single column, you can see whether there’s consistent behavior among your cohorts, or whether performance improves or deteriorates. As you look down the column at data for each newer cohort, you’re looking forward in time (for example, Day 5 for the second cohort occurs after Day 5 for the first cohort though they appear in the same column).

If you’re evaluating daily data, you can look at a single column, say the Day 5 column, to see whether all cohorts perform at about the same level at that point in their experience, or whether the data indicate improving or deteriorating trends. For example, if you are retaining the same percentage of users across all cohorts at Day 5, then that can indicate a comforting consistency in user experience. On the other hand, if you see a steady increase in retention at Day 5, you might be able to correlate that with an improvement in your content or an upgrade to the speed at which your app performs. A steady deterioration of user retention at Day 5 might indicate stale content, or an unusually difficult or poorly coded level in a game--something that is causing fewer and fewer users to continue with the experience.

Engagement, retention, and acquisition

Understanding the point at which users tend to disengage (for example, initiate fewer sessions, view fewer pages, generate less revenue) can help you identify two things:

• Common points of attrition that might be easily remedied
• The rate at which you need to acquire new users to compensate for unavoidable attrition

For example, if you notice that revenue regularly starts to decline in the third or fourth week after acquisition, you might reengage users with a remarketing or email campaign that offers discounts or ads for new products that have been added since their last sessions. You could also reengage those users with dynamic remarketing by offering ads for products related to the ones they purchased during their initial engagement.

If you identify inevitable patterns of attrition, say 10% a month, then you are able to understand the rate at which you need to acquire new users to create the growth rate you want for your business.

Response to short-term marketing efforts

If you run short-term marketing efforts like single-day email campaigns, this report gives you the chance to track the behavior of just the users you acquired during the related time frames. For example, if you’re running successive 30%-off, 25%-off, and 20%-off campaigns as a holiday approaches, you can see how different metrics like Revenue per User andTransactions per User compare among the groups of users you acquired on the dates each campaign ran.

Content From Google.com

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PageRank Dead? – What Will Replace the "Green Standard?"

The first time that SEOs were faced with the prospect of PageRank going away was a year ago, when Matt Cuts suggested that there would be no further updates in 2013.

Source: SEORoundtable.com

Later that same month, Cutts indicated the pipeline that pushes PageRank data from the internal Google servers to the toolbar broke and there were no plans to fix it. It was widely speculated at that time that there would never be another PageRank update. But just six weeks later, PageRank updated on December 6.
Fast-forward exactly one year after the first scare to October 6, 2014, and Googler John Mueller got everyone’s attention when he stated, "We’ll probably not…be updating it [PageRank] moving forward." You will find this at 20:30 in the following Google+ Hangout video.
So…that means one of two things. Google may or may not update the PageRank bar in the future. Regardless of what happens in the short term, now is the time to consider alternatives.
PageRank is a nice link-building metric to show clients, because most are familiar with it. That said, with the advent of Hummingbird and the ability of Google to understand relationships, relevance is every bit as important as PageRank. In fact, I think a strong argument can be made that moving forward, relevance will become increasingly important – much more so than PageRank alone.
Where I find PageRank most useful is in performing link audits. By using PageRank, we can easily spot low-value and spammy links with filters like Homepage PR0 or Gray + Internal Page PR0 or Gray. This metric becomes less reliable, as the frequency of the updates lag.

see here for details ,http://www.youtube.com/watch?feature=player_embedded&v=-GlxLlpm3Ew#t=0

How Is PageRank Calculated?

PageRank is calculated using a logarithmic scale (similar to the Richter scale), with a value ranging from 0 to 10. It has an estimated base of 4-5. In other words, assuming a base of 5, PR2 links are comparable to 5 PR1 links; a PR6 link is comparable to 5 PR5 links, and so on.

What Are the Alternatives?

Just about every backlink reporting service has developed a metric to compete with PageRank. In this post, I’ll review the four resources that I have found to be the best alternatives to PageRank. That group includes Moz, Majestic, Ahrefs, and Blekko.

MOZ – Page Authority

According to Moz, Page Authorityis a calculated metric designed to predict how well a given Web page is likely to rank in Google’s SERPs. It uses a machine learning model to find an algorithm which correlates with rankings across search results to make that prediction.

Page Authority is scored on a 100-point, logarithmic scale. As with PageRank, it's MUCH easier to grow your score from 10 to 20 than it would be to grow it from 30 to 40. Regular updates sometimes lead to a fluctuation in score. For comparison sake, Searchenginewatch.com has a Page Authority of 92 (vs a PR7).

Majestic – Trust Flow

According to Majestic, the Trust Flow metric originates from a large pool of manually reviewed Web pages. This by no means includes every trusted website on the Internet, yet appears to provide an adequate base. Their rationale is that trustworthy websites tend to link to other trustworthy sites. Those neighbors, in turn, have a tendency to link to other trustworthy neighbors and so on.

Dixon Jones from Majestic commented recently that "If you want the strongest metric of quality, then I think you should look at Trust Flow." For comparison sake, Searchenginewatch.com has a Trust Flow of 54 (vs. a PR7).

Ahrefs URL Rank

According to Ahrefs, this scale is derived from measuring the impact of all backlinks with varying link equity to a given page. Using the example of a page with a URL rank of 100 and 10 do-follow links:
The Ahrefs algorithm allows for no more than 80 percent of its rating to pass through to another page. That means a page with the rating of 100 passes through a rating of 80. This rating 80 gets divided between the do-follow links. In this example, each link will get a pass-through value of eight.

This process is repeated over and over for every page. Internal links are also included in the rating calculation. As a general rule, the more backlinks a given URL has - the higher URL Rank it receives. That said, it’s not strictly a numbers game. One high-value link can give more boost to ranking than hundreds of spammy backlinks. For comparison sake, Searchenginewatch.com has a URL Rank of 85 (vs. a PR7).

Blekko – Host Rank

Unlike the others, Host Rank is on a linear scale, rather than a logarithmic scale. The score can range from 0 to unlimited. For benchmarking purposes, Google has a Host Rank of 16,735 as compared to Search Engine Watch with a Host rank of 1,370. Keeping in mind these sites respectively have a PageRank of 9 and 7, by using the Blekko scale, it is much easier to visualize the actual spread between the two websites.

Unfortunately, Blekko no longer offers a site explorer feature, so one must rely upon their API or use a third-party toolbar browser extension.

The Takeaway

Moz, Majestic, and Ahrfs have all done a pretty decent job in mimicking the PageRank algorithm. Dixon Jones, of Majestic, reports the following level of correlation between Google PageRank and other scales:
Moz Page Authority correlate at 0.68 (2012 Figure)
Majestic Trust flow correlates at 72.79 percent
Ahrefs Domain Rank correlates at 76.32 percent (He did not provide URL rank)

Any one of them, used consistently, should work fine for comparative purposes. For the added linear dimension and quick understanding of relative trust and authority among sites, I recommend mixing in Blekko.

all content from:- http://searchenginewatch.com/article/2376451/PageRank-Dead-What-Will-Replace-the-Green-Standard

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