February, 2015 vs. February, 2014: Changes in Statistics of Yandex Commercial Queries

Русская версия

Early in the year 2015, basing on numerous mass media reports, many business undertakings faced reduction in demand for their products – goods and services. Negative economic events caused by sanctions against Russia and dropping of prices for utilities could lead to reduced demand in Internet both for B2C and B2B consumer goods.

Search engines including Yandex - the popular one - allow tracking changes in popularity of search queries related to commercial activities in Internet.

 

 

February, 2015 vs. February, 2014: Changes in Statistics of Yandex Commercial Queries

A.P. Rototayev, P.V. Yevseyev

NOVA Promotions Advertising Agency

1. Objectives of the Survey. 2

2. Key Terms, References, Methods. 3

3. General Trends of Changes in Statistics of Yandex Commercial Queries from December, 2013 to February, 2015  5

3.1. Changes in Yandex Traffic - February, 2014 vs. February, 2015. 5

3.2. Changes in “Buy” Query from December, 2013 till February, 2015. 5

4. Changes in “Buy Goods” Statistics, “Moscow” - February, 2015 vs. February, 2014. 7

4.1. Consumer Electronics, Brands. 8

4.2. Household Appliances. 9

4.3. Real Estate. 9

4.4. Vehicles. 10

4.5. Furniture. 11

4.6. Sports Goods. 11

4.7. Household Items. 12

4.8. Tools, Machinery. 13

5. Changes in “Sell Goods” Query Statistics, “Moscow”- February, 2015 vs. February, 2014. 14

5.1. Changes in “Sell Goods” per Categories. Figures. 15

6. Key Takeaways. 20

7. Annex. Tables. 21

1. Objectives of the Survey

The central objective of this survey was collection and analysis of Yandex search statistics related to changes in popularity of the selected search queries caused by changes in market demand and supply of consumer goods in Internet.

 

2. Key Terms, References, Methods

Search query (“Query”) is a phrase, word or saying typed by user in a search engine bar to get a list of websites selected by the search engine (pages of search results).

Number of user queries to Yandex, the most popular Runet search engine, can be assessed by the number of displays of the search results page according to the query (“Number of displays”) for a certain period of time, e.g. a month.

Search engine specialists often use a “frequency” term being quantitatively determined by the number of displays of the search results page per month. Such number of displays may be determined for each “region” from a list offered by Yandex, and may or may not take into account the number of displays on related queries, e.g. by using another case of the words in query or adding qualifying words.

In this survey, “Frequency” of a query shall stand for a general number of displays of the search results page both on the query itself and related queries that include the main query in any case and declensions, with any word order, as well as additional words and symbols.

To assess frequency, Yandex.Wordstat service data on popularity of search queries (frequency) in Yandex search engine (https://wordstat.yandex.ru) for “Moscow” region was used. Commercial queries of two types, i.e. “buy goods”, e.g. “buy smartphone”, and “sell goods”, e.g. “sell a car” were applied.

A history of “buy” query frequency change (without additional words) in Moscow and Russia was assessed separately. The “buy” query frequency (without additional words) includes, inter alia, frequency of “buy goods” commercial queries.

February, 2014 and February, 2015 were chosen as a period of time for comparison.

72 queries (Tables 1, 2 and Annex), divided into the following categories, were selected:

  1. Consumer electronics, brands
  2. Household appliances
  3. Real estate
  4. Vehicles
  5. Furniture
  6. Sports goods
  7. Clothes, footwear, household items
  8. Tools

It should be noted that some queries can be referred to B2B (partially or fully).

Information on Yandex query frequency in February, 2014 and February, 2015 for “Moscow” was collected, as well as information on the “buy” query frequency (without specifying the goods and any additional words) in “Russia” and “Moscow” within a period from December, 2013 to February, 2015.

Yandex visiting statistics for February, 2014 and February, 2015 was gathered. Yandex.Statistics (https://stat.yandex.ru) was also used to gather visiting statistics data.

 

3. General Trends of Changes in Statistics of Yandex Commercial Queries

from December, 2013 to February, 2015

 

3.1. Changes in Yandex Traffic - February, 2014 vs. February, 2015

Changes in query statistics can be influenced by various factors, including changed popularity of any certain search engine (Yandex) or general decrease in the search system hits. We turned our attention to the fact that total number of people visited Yandex search engine in Russia (to be exact, in “Russia” region) from February, 2014 to February, 2015 dropped from 50453550 to 49691780, or by 1.51%.

Total number of Yandex queries in “Moscow” region from February, 2014 to February, 2015 lowered from 5607860 to 5533450, or by 1.32%. This survey didn’t make it a point to find the reason of such decrease, but we should take this fact into account. The total search engine visiting statistics is likely influenced by the changed customer demand in Internet as well.

 

3.2. Changes in “Buy” Query from December, 2013 till February, 2015

“Buy” query statistics is of peculiar interest. This query doesn’t contain data on a product or a service but it is likely that the “buy” query change dynamics correlates with Internet customer demand to some extent as all commercial queries with the word “buy” are considered in a total displays of the search engine results on the “buy” query.

The “buy” query statistics in Russia (Fig. 1) reflects the maximum frequency values for December, 2014 and a dropped value for the summer due to season of vacations. We observe the same situation in Moscow (Fig. 2), including the declined frequency in January-February, 2015 vs. December, 2014; however, the rate of decrease in Moscow is significantly higher.

It can be assumed that the difference in frequency dynamics in Moscow, on the one hand, and in Russia as a whole, on the other hand, is caused by major loss in business activity owing to B2B, which is more widely represented in Moscow as compared to the regions. This assumption should be checked.

“Buy” query frequency from February, 2014 to February, 2015 gained 0.22% in Russia (from 56339832 to 56463816 displays) and lost 25.56% in Moscow (from 19167766 to 14267854 displays) (Table 1).

 “Buy” Query Frequency in Yandex (“Russia”)

Figure 1. History of “Buy” Query Displays per Month (Frequency) from December, 2013 to February, 2015 in Russia

 

“Buy” Query Frequency in Yandex (“Moscow”)

Figure 2.  History of “Buy” Query Displays per Month (Frequency) from December, 2013 to February, 2015 in Russia

 

4. Changes in “Buy Goods” Statistics, “Moscow” - February, 2015 vs. February, 2014

Frequency of the selected queries such as “buy goods” is shown in Table 4 (see Annex).

Out of 72 selected “buy goods” queries, frequency in Moscow for February, 2015 turned out to be lower than a year ago for the majority of queries. Decline in frequency of 43 queries exceeded 5%, while frequency of only 17 queries increased more than by 5% (Fig. 3). It is to be recalled that February decrease of Yandex visiting in Moscow amounted to 1.32% for the same period (item 3.1).

Number of “Buy Goods” Queries - February, 2015 vs. February, 2014

 

Some of the queries have shown a significant drop. That is not to say that this drop was unexpected. On the contrary, it correlates with the market situation in some ways and reflects the customer demand in Internet.

Let’s consider the data per categories.

 

4.1. Consumer Electronics, Brands

Along with consumer goods, which can be classified as “consumer electronics”, we reviewed brands as well. It’s worthy of note that some brands offer the goods from other categories.

The interesting point is that demand for some goods in Internet (“buy smartphone”) increased for the year, while popularity of “high-ticket” brands, judging by frequency of the corresponding queries, faced a decline. Out of the brands reviewed, only Lenovo demonstrated growth with its more democratic pricing policy.

Unusually dramatic frequency loss of “buy iphone” and “buy ipad” queries may speak for accelerated number of Apple potential buyers and lowered demand for the high-ticket goods.

Changes in “Buy Goods” Frequency for the Category “Consumer Electronics, Brands”

Figure 4. Frequency of Queries in “Consumer Electronics, Brands” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

4.2. Household Appliances

Frequency of queries in the “household appliances” category vs. “consumer electronics” showed lower decline per one type of goods and gain in approximately half of the reviewed cases (Fig. 5). Frequency of queries “buy household appliances”, “buy washing machine” and other has gone up.

The obtained data doesn’t show any reduction in consumer demand for household appliances in February, 2015 year-on-year.

Change in “Buy goods” Frequency for the Category “Household appliances”

Figure 5. Frequency of Queries in “Household appliances” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

4.3. Real Estate

Popularity of the “buy apartment” query has significantly lowered in the “Real estate” category. Query frequency for “Moscow” region declined approximately by 1.6 times from February, 2014 to February, 2015. Frequency of the “buy country house” and “buy land plot” queries lost its positions as well. It’s likely that this data reflects the general situation at the real estate market and a “wait-and-see” customer policy. Reduced real income of the population and other factors could also contribute to possible decrease in the real estate demand.

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Figure 6. Frequency of Queries in “Real estate” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

4.4. Vehicles

Popularity of “buy car”, “buy motor car” and “buy automobile” queries significantly decreased in February, 2015 (approximately by 5-20%). These changes support numerous mass media reports on deterioration of the RF car market situation. 

Change in “Buy Goods” Frequency for the Category “Vehicles”

Figure 7. Frequency of Queries in “Vehicles” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

4.5. Furniture

Frequency of the “buy furniture” query in February, 2015 slid down by 9% (vs. February, 2014), but frequency of the “buy bed”, “buy wardrobe” and other queries raised. Frequency of the “buy computer desk” showed a 14% reduction, while frequency of the “buy computer” query in “Consumer electronics” category lowered by 15%. In general, our scarce data doesn’t allow drawing any conclusion on decrease in Internet furniture market. 

Change in “Buy Goods” Query Frequency for the Category “Furniture”

Figure 8. Frequency of Queries in “Furniture” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

4.6. Sports Goods

In “Sports goods” category, we revealed an impaired popularity of the “buy home gym” and “buy snowboard” queries, while the search “demand” for ski, backpacks and bicycles gained several percent.

Change in “Buy goods” Query Frequency for the Category “Sports Goods”

Figure 9. Frequency of Queries in “Sports Goods” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

4.7. Household Items

Out of eight queries in “household items” category, maximum decrease (-24%) has been shown by the “buy trendy clothes”, while maximum growth (+13%) falls on the “buy boots”. “Buy watches” query has significantly slid down (-14%). It might happen that we observe the abovementioned tendency of demand decline in relation to more expensive goods beyond daily demand.

Change in “Buy goods” Query Frequency for the Category “Household Items”

Figure 10. Frequency of Queries in “Household Items” - February, 2015 vs. February, 2014 (February as % of February 2014)

 

4.8. Tools, Machinery

“Buy power tool” query demonstrated a stable growth with its frequency in February, 2015 exceeding that for February, 2014 by 34%. “Buy tools” query frequency decreased, but frequency of the “buy” queries per some items (drill, petrol-powered saw) advanced (Fig. 11).

Change in “Buy Goods” Query Frequency for the Category “Tools, Machinery”

Figure 11. Frequency of Queries in “Tools, Machinery” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

 

5. Changes in “Sell Goods” Query Statistics, “Moscow”- February, 2015 vs. February, 2014

Out of 72 selected “sell goods” queries, frequency in Moscow for February, 2015 turned out to be lower than that for majority of the queries a year ago. The situation reminds the “buy goods” queries. Frequency of 42 queries lost more than 5%, while frequency of 17 queries gained more than 5% (Fig. 12).

Number of “Sell Goods” queries – February, 2015 vs. February, 2014

 

It’s likely that the situation speaks for the general loss of business activity in Moscow. Potential sellers may wait until the market and currency rate are stabilized. First of all, this is true for expensive goods such as real estate, vehicles and luxury brands.

Frequency changes of the “sell goods” queries per different categories are shown in Fig. 13-20.

As for separate queries, increased “sell macbook” (+32% in February, 2015 year-on-year), “sell bake oven” (+75%) and “sell microwave oven” (+126%) are worth mentioning in terms of monthly displays. The “decreased” queries include “sell ipad” (-66%), “sell sony vaio” (-72%), “sell apartment” (-39%) and “sell car” (-38%).

 

5.1. Changes in “Sell Goods” per Categories. Figures

 Change in “Sell Goods” Frequency for the Category “Consumer Electronics, Brands”,

Figure 13. Frequency of Queries in “Consumer Electronics, Brands” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

 

Frequency of Queries in “Household Appliances”- February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

Figure 14. Frequency of Queries in “Household Appliances”- February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014) 

 

Change in “Sell Goods” Frequency for the Category “Real estate"

 Figure 15. Frequency of Queries in “Real estate”- February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

 

Change in “Sell Goods” Frequency for the Category “Vehicles"

 

Figure 16. Frequency of Queries in “Vehicles” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

Change in “Sell Goods” Frequency for the Category “Furniture"

Figure 17. Frequency of Queries in “Furniture” – February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

Change in “Sell Goods” Frequency for the Category “Sports Goods"

Figure 18. Frequency of Queries in “Sports Goods” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

Change in “Sell Goods” Frequency for the Category “Household Items”

Figure 19. Frequency of Queries in “Household items” in February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

Change in “Sell Goods” Frequency for the Category “Tools”

Figure 20. Frequency of Queries in “Tools” - February, 2015 vs. February, 2014 (February, 2015 as a percentage ratio of February, 2014)

 

 6. Key Takeaways

The number of displays of the search results page according to user queries in Yandex decreased in February, 2015 year-on-year in relation to some commercial queries of “buy goods” type. The revealed changes may result from lowered consumer demand in certain categories of B2C goods.

Analysis of collected data may also speak for the overall decline in business activity, including B2B sector, which is subject to further investigation.

 

7. Annex. Tables

Table 1. List of “Buy Goods” Queries Selected for Gathering of Frequency Statistics for February, 2014 and February, 2015

 

Item Search Query
1 buy iphone
2 buying iphone
3 buy ipad
4 buy TV
5 buy computer
6 buy laptop
7 buy phone
8 buy samsung
9 buy pad
10 buy samsung phone
11 buy galaxy
12 buy htc
13 buy playstation
14 buy xbox
15 buy dvd
16 buy macbook
17 bying macbook
18 buy mac
19 buy lenovo
20 buy sony vaio
21 buy netbook
22 buy smartphone
23 buy ipod
24 buy smart tv
25 buy printer
26 buy music centre
27 buy home theatre
28 buy household appliances
29 buy washing machine
30 buy coffee-maker
31 buy air conditioner
32 buy cooker
33 buy vacuum-cleaner
34 buy bake oven
35 buy coffee machine
36 buy fridge
37 buy microwave oven
38 buy ski
39 buy snowboard
40 buy backpack
41 buy bicycle
42 buy home gym
43 buy apartment
44 buy country house
45 buy land plot
46 buy car
47 buy motor car
48 buy automobile
49 buy powerboat
50 buy boat
51 buy motorcycle
52 buy furniture
53 buy bed
54 buy mattress
55 buy sofa
56 buy wardrobe
57 buy computer desk
58 buy boots
59 buy trendy clothes
60 buy watch
61 buy tea/ coffee set
62 buy guitar
63 buy piano
64 buy books
65 buy knife
66 buy power tool
67 buy tools
68 buy drill
69 buy carpenter's bench
70 buy perforator
71 buy set of tools
72 buy petrol-powered saw

 

Table 2. List of “Sell Goods” Queries Selected for Gathering of Frequency Statistics for February, 2014 and February, 2015

 

Item Search Query
1 sell iphone
2 selling iphone
3 sell ipad
4 sell TV
5 sell computer
6 sell laptop
7 sell phone
8 sell samsung
9 sell pad
10 sell samsung phone
11 sell galaxy
12 sell htc
13 sell playstation
14 sell xbox
15 sell dvd
16 sell macbook
17 selling macbook
18 sell mac
19 sell lenovo
20 sell sony vaio
21 sell netbook
22 sell smartphone
23 sell ipod
24 sell smart tv
25 sell printer
26 sell mini system
27 sell home theatre system
28 sell household appliances
29 sell washing machine
30 sell coffee-maker
31 sell air conditioner
32 sell cooker
33 sell vacuum cleaner
34 sell bake oven
35 sell coffee machine
36 sell fridge
37 sell microwave oven
38 sell ski
39 sell snowboard
40 sell backpack
41 sell bicycle
42 sell home gym
43 sell apartment
44 sell country house
45 sell land plot
46 sell car
47 sell motor car
48 sell automobile
49 sell powerboat
50 sell boat
51 sell motorcycle
52 sell furniture
53 sell bed
54 sell mattress
55 sell sofa
56 sell wardrobe
57 sell computer desk
58 sell boots
59 sell trendy clothes
60 sell watch
61 sell tea/ coffee set
62 sell guitar
63 sell piano
64 sell books
65 sell knife
66 sell power tool
67 sell tools
68 sell drill
69 sell carpenter's bench
70 sell perforator
71 sell set of tools
72 sell petrol-powered saw

 

Table 3. History of Daily Displays (Frequency) of “Buy” Queries in December, 2013 – February, 2015

 

Month “Buy” Frequency in Russia “Buy” Frequency in Moscow
December, 2013 53014428 18786507
January, 2014 59163653 19249983
February, 2014 56339832 19167766
March, 2014 61146751 20222976
April, 2014 53926252 17730817
May, 2014 49033729 16003965
June, 2014 47079699 15353877
July, 2014 44151093 13021470
August, 2014 51612777 14409435
September, 2014 55237154 15536795
October, 2014 62310606 17003520
November, 2014 61009591 16512731
December, 2014 68714405 18728987
January, 2015 58349520 13952934
February, 2015 56463816 14267854

 

Table 4. “Buy Goods” Frequency and Changes in February, 2015 vs. February, 2014

 

Query Group Search Query 2015 changes vs. 2014, % Frequency – February, 2014 Frequency – February, 2015
Consumer Electronics buy iphone -92 516302 40984
buying iphone -14 77466 66862
buy ipad -96 197426 7870
buy TV -9 80371 73473
buy computer -15    
buy laptop -13 68865 59583
buy phone -12 121839 107076
buy samsung -18 70685 57906
buy pad -28 69132 49791
buy samsung phone -11 9059 8035
buy galaxy -22 36886 28910
buy htc -32 21702 14832
buy playstation -60 19744 7897
buy xbox -24 25777 19498
buy dvd -24 14463 10979
buy macbook -19 2010 1637
bying macbook -37 4559 2893
buy mac -34 4749 3131
buy lenovo 12 16146 18072
buy sony vaio -33 2953 1968
buy netbook -43 8661 4913
buy smartphone 14 29938 34060
buy ipod -40 6855 4103
buy smart tv -20 1073 862
buy printer -1 34981 34492
buy mini system -5 3198 3042
buy home theatre system -28 2115 1523
Household Appliances buy household appliances 10 3113 3420
buy washing machine 27 36546 46461
buy coffee-maker 1 10374 10499
buy air conditioner -13 15653 13640
buy cooker 13 33154 37463
buy vacuum-cleaner -12 33398 29384
buy bake oven 10 6071 6673
buy coffee machine -15 6051 5151
buy fridge 5 53504 56299
buy microwave oven 22 7062 8616
Sports Goods buy ski 3 20835 21478
buy snowboard -20 9993 7991
buy backpack 4 24093 25000
buy bicycle 5 48731 51054
buy home gym -12 18389 16145
Real Estate buy apartment -38 1157604 715516
buy country house -7 1808 1676
buy land plot -11 119820 106622
Vehicles buy car -10 182286 163972
buy motor car -22 81812 64097
buy automobile -4 158240 152121
buy powerboat -11 5573 4985
buy boat 6 23030 24362
buy motorcycle -6 35964 33900
Furniture buy furniture -9 67332 61013
buy bed 9 74635 81528
buy mattress 7 37081 39832
buy sofa 0 90549 90826
buy wardrobe 11 48984 54540
buy computer desk -14 7907 6773
Clothes, Footwear, Household Items buy boots 13 20458 23116
buy trendy clothes -24 1222 928
buy watch -14 117605 101464
buy tea/ coffee set 8 5693 6121
buy guitar -10 24582 22069
buy piano 8 6057 6528
buy books -9 154718 140629
buy knife -5 64004 60789
Tools and Machinery buy power tool 34 25943 34673
buy tools -16 26301 22147
buy drill 19 4344 5157
buy carpenter's bench 4 50 52
buy perforator -7 4625 4317
buy set of tools -3 3297 3203
buy petrol-powered saw 14 6967 7969

 

Table 5. “Sell Goods” Frequency and Changes in February, 2015 vs. February, 2014

 

Query Group Query Dynamics, % Frequency 2014 Frequency 2015
Consumer
Electronics
sell iphone -29 2057 1457
selling iphone -9 2410 2201
sell ipad -66 953 323
sell TV 15 2116 2426
sell computer -21 1627 1288
sell laptop 0 2814 2823
sell phone -25 5402 4041
sell samsung -26 1749 1287
sell pad -20 1290 1038
sell samsung phone -27 123 90
sell galaxy -25 633 476
sell htc -28 463 333
sell playstation -15 285 242
sell xbox -22 564 442
sell dvd -32 239 163
sell macbook 32 79 104
selling macbook -27 362 263
sell mac -29 215 153
sell lenovo -1 355 353
sell sony vaio -72 363 102
sell netbook -44 226 126
sell smartphone -10 446 403
sell ipod -38 197 123
sell smart tv   0 3
sell printer -3 939 915
sell mini system 4 98 102
sell home theatre system 11 54 60
Household Appliances sell household appliances 14 266 302
sell washing machine 1 1165 1180
sell coffee-maker -22 147 115
sell air conditioner -38 396 246
sell cooker -17 785 653
sell vacuum cleaner -4 364 350
sell bake oven 75 8 14
sell coffee machine 4 23 24
sell fridge 13 1358 1531
sell microwave oven 126 31 70
Sports Goods sell ski 7 730 784
sell snowboard -24 356 271
sell backpack 6 196 207
sell bicycle -6 1210 1139
sell home gym -26 447 333
Real Estate sell apartment -39 100774 61845
sell country house -4 129 124
sell land plot -25 27187 20411
Vehicles sell car -31 15730 10915
sell motor car -38 17431 10855
sell automobile -17 21770 17989
sell powerboat 13 482 544
sell boat -23 1169 904
sell motorcycle -17 1584 1307
Furniture sell furniture -15 3062 2615
sell bed -18 1203 981
sell mattress -14 227 196
sell sofa -20 1294 1040
sell wardrobe -16 925 773
sell computer desk -37 95 60
Clothes, Footwear, Household Items sell boots -25 386 291
sell trendy clothes   0 0
sell watch -15 4079 3463
sell tea/ coffee set 10 176 193
sell guitar 15 824 945
sell piano -14 980 840
sell books -5 6990 6674
sell knife 1 863 871
Tools and Machinery sell power tool -1 3120 3101
sell tools 29 529 682
sell drill   0 47
sell carpenter's bench   0 0
sell perforator -31 103 71
sell set of tools   0 70
sell petrol-powered saw 39 145 202

 

 

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